diff --git a/sleep_assessment/cases/case1/case1_01.json b/sleep_assessment/cases/case1/case1_01.json deleted file mode 100644 index 856c183d8a167f648bbcbdb9e7c1b99b607acbf2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_01.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/ISRUC_01.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 5.5, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_02.json b/sleep_assessment/cases/case1/case1_02.json deleted file mode 100644 index 551aa4180a58609764e0fe9653a8b20c3f911a8d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_02.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/ISRUC_02.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 55.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_03.json b/sleep_assessment/cases/case1/case1_03.json deleted file mode 100644 index 31701d7d40f4be49f88af9141b7b94eab4195bc5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_03.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/ISRUC_03.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 3.5, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_04.json b/sleep_assessment/cases/case1/case1_04.json deleted file mode 100644 index 29aa3f6515b3e377c17e6647a369f64e6ce9475f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_04.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/ISRUC_04.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 1.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_05.json b/sleep_assessment/cases/case1/case1_05.json deleted file mode 100644 index d77500a4e69e7acf2c0d9995972d5ea4fc3b8458..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_05.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/ISRUC_05.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 75.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_06.json b/sleep_assessment/cases/case1/case1_06.json deleted file mode 100644 index af6e9a26287715c29787f5a3c56f8742ff1642ed..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_06.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/HMC_01.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 4.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_07.json b/sleep_assessment/cases/case1/case1_07.json deleted file mode 100644 index f118ded3d54196df439a1c314d6c83ab42e0f9df..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_07.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/HMC_02.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 3.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_08.json b/sleep_assessment/cases/case1/case1_08.json deleted file mode 100644 index 11b3292f117ef35620028d0944fedba60f6024ae..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_08.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/HMC_03.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 1.5, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_09.json b/sleep_assessment/cases/case1/case1_09.json deleted file mode 100644 index 30dfc6dbd3346fd16caa203f62f7dcff1f941f99..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_09.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/HMC_04.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 3.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_10.json b/sleep_assessment/cases/case1/case1_10.json deleted file mode 100644 index bdaca0ec5a138f750040457d96afa6f68bfdd44c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_10.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/HMC_05.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 22.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_11.json b/sleep_assessment/cases/case1/case1_11.json deleted file mode 100644 index a72386ed162819b9ea58d5fd15c0c363837d4b6f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_11.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/SHHS1_01.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 14.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_12.json b/sleep_assessment/cases/case1/case1_12.json deleted file mode 100644 index a4f57e20ac397906a8b668055ea2339bc29d1743..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_12.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/SHHS1_02.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 0.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_13.json b/sleep_assessment/cases/case1/case1_13.json deleted file mode 100644 index 099c3452496c2ba86ba8aa249e19fb049373e509..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_13.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/SHHS1_03.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 83.5, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_14.json b/sleep_assessment/cases/case1/case1_14.json deleted file mode 100644 index e9e52dbfb67f74f9073632a0994b00b9aa3ef7cc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_14.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/SHHS1_04.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 41.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_15.json b/sleep_assessment/cases/case1/case1_15.json deleted file mode 100644 index d5c91122d07c521a0530d4112a360e2791b91c83..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_15.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/SHHS1_05.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 10.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_16.json b/sleep_assessment/cases/case1/case1_16.json deleted file mode 100644 index 442ab0c15c811b5bbd8a75d556b53d1c9c5bb247..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_16.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/MASSSS3_01.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 3.5, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_17.json b/sleep_assessment/cases/case1/case1_17.json deleted file mode 100644 index f758c7471e724d71fc66d9457afde45399dbd27f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_17.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/MASSSS3_02.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 21.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_18.json b/sleep_assessment/cases/case1/case1_18.json deleted file mode 100644 index 647b67fdfd0c53f080eb1ecd9afb1ebf64138818..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_18.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/MASSSS3_03.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 7.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_19.json b/sleep_assessment/cases/case1/case1_19.json deleted file mode 100644 index df74312d3659193edc99990903b0d5aa0e4de81d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_19.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/MASSSS3_04.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 2.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_20.json b/sleep_assessment/cases/case1/case1_20.json deleted file mode 100644 index 67ab17e447a33f00346166f61f7476c9ade492f3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_20.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/MASSSS3_05.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 7.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_21.json b/sleep_assessment/cases/case1/case1_21.json deleted file mode 100644 index e1cd3431b7f3749c7673ada0f4105d8f27f9a15c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_21.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/Physionet2018_01.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 4.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_22.json b/sleep_assessment/cases/case1/case1_22.json deleted file mode 100644 index 507c040797260415ce3a91f64a26ff5718e3afea..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_22.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/Physionet2018_02.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 10.5, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_23.json b/sleep_assessment/cases/case1/case1_23.json deleted file mode 100644 index e9f3c90211d7dcafab0eb7588085a115324b01c3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_23.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/Physionet2018_03.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 4.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_24.json b/sleep_assessment/cases/case1/case1_24.json deleted file mode 100644 index 66c036c7472a64580859297774f77d570dedcc74..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_24.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/Physionet2018_04.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 0.5, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case1/case1_25.json b/sleep_assessment/cases/case1/case1_25.json deleted file mode 100644 index 124296a400b0b16b8848bd54f4c2c21704b894fb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case1/case1_25.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Onset Latency (SOL) for the provided sleep EEG/PSG recording. SOL is defined as the time in minutes from the first scored 30-second epoch to the first sleep epoch (N1, N2, N3, or REM). Please clearly state the final SOL value in minutes in your response.", - "label_path": "data/sleep/Physionet2018_05.npy" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Onset Latency (SOL) value in minutes from the agent's report. SOL is defined in this benchmark as the time from the first scored 30-second epoch to the first sleep epoch.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key in the JSON must be EXACTLY \"sleep_onset_latency_min\".\n5. The value must be a FLOAT in minutes.\n6. If the value is missing or cannot be found, return {\"sleep_onset_latency_min\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_onset_latency_min\": }", - "metrics": [ - { - "metric_id": "sleep_onset_latency_accuracy", - "type": "numeric_check", - "target_key": "sleep_onset_latency_min", - "weight": 100, - "params": { - "gt_value": 1.0, - "tolerance": 2.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_01.json b/sleep_assessment/cases/case10/case10_01.json deleted file mode 100644 index 24217384dbbd5bd185bce2e3dec6a52675d8cd84..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_01.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 14, - "tolerance": 0.7000000000000001 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 8.142857142857142, - "tolerance": 0.40714285714285714 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_02.json b/sleep_assessment/cases/case10/case10_02.json deleted file mode 100644 index eceda8edf330fd37794d32dfca303a2a4422a915..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_02.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 7, - "tolerance": 0.35000000000000003 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 10.928571428571429, - "tolerance": 0.5464285714285715 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_03.json b/sleep_assessment/cases/case10/case10_03.json deleted file mode 100644 index 6b52c388d79815b64b6254c2e45c342890eed809..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_03.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 5, - "tolerance": 0.25 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 17.2, - "tolerance": 0.86 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_04.json b/sleep_assessment/cases/case10/case10_04.json deleted file mode 100644 index 57dd95a0a0619a0d775ac4ced20c4ef5335ec2bb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_04.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 8, - "tolerance": 0.4 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 13.375, - "tolerance": 0.6687500000000001 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_05.json b/sleep_assessment/cases/case10/case10_05.json deleted file mode 100644 index 5ba4764acf525a53d9c3df8bcac7b36bfb7385c0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_05.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 5, - "tolerance": 0.25 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 16.3, - "tolerance": 0.8150000000000001 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_06.json b/sleep_assessment/cases/case10/case10_06.json deleted file mode 100644 index b7d2b03bc331a15f75f52c9dafe580d472b94cbd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_06.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 6, - "tolerance": 0.30000000000000004 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 1.75, - "tolerance": 0.08750000000000001 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_07.json b/sleep_assessment/cases/case10/case10_07.json deleted file mode 100644 index 9da0b796f2e57fba1797120b03661bb694c05ab2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_07.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 14, - "tolerance": 0.7000000000000001 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 7.821428571428571, - "tolerance": 0.39107142857142857 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_08.json b/sleep_assessment/cases/case10/case10_08.json deleted file mode 100644 index 26cb2739386fbd0ac8f4d86d86ab7cd0b5392c55..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_08.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 14, - "tolerance": 0.7000000000000001 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 5.321428571428571, - "tolerance": 0.26607142857142857 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_09.json b/sleep_assessment/cases/case10/case10_09.json deleted file mode 100644 index 3045b40d08e9a956a6ecff4c2b50980b0c5e4b9d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_09.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 8, - "tolerance": 0.4 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 3.9375, - "tolerance": 0.19687500000000002 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_10.json b/sleep_assessment/cases/case10/case10_10.json deleted file mode 100644 index 7e4f037fa0c0e8342e0cc0a61a078525ae26cfe7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_10.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 10, - "tolerance": 0.5 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 7.15, - "tolerance": 0.35750000000000004 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_11.json b/sleep_assessment/cases/case10/case10_11.json deleted file mode 100644 index b1d3b5e09e0c64393a9ad7a1e3509983a81151ea..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_11.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 15, - "tolerance": 0.75 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 4.366666666666666, - "tolerance": 0.21833333333333332 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_12.json b/sleep_assessment/cases/case10/case10_12.json deleted file mode 100644 index ffe41d72b8fc8cbeda9635f65eb0ad5b04498bdb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_12.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 12, - "tolerance": 0.6000000000000001 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 1.7916666666666667, - "tolerance": 0.08958333333333335 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_13.json b/sleep_assessment/cases/case10/case10_13.json deleted file mode 100644 index ffab9541eed4b242a9cb1e8c7961d91f46b97908..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_13.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 18, - "tolerance": 0.9 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 8.222222222222221, - "tolerance": 0.4111111111111111 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_14.json b/sleep_assessment/cases/case10/case10_14.json deleted file mode 100644 index 67b65546e4dcdfc099e5522c62630141204b5127..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_14.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 17, - "tolerance": 0.8500000000000001 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 8.588235294117647, - "tolerance": 0.4294117647058824 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_15.json b/sleep_assessment/cases/case10/case10_15.json deleted file mode 100644 index eb84379289a48565ed84c99a6352054ba9601ecc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_15.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 13, - "tolerance": 0.65 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 3.4615384615384617, - "tolerance": 0.1730769230769231 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_16.json b/sleep_assessment/cases/case10/case10_16.json deleted file mode 100644 index ad7244442f494477edea581b5a8a3dfe4d1f4c9e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_16.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 26, - "tolerance": 1.3 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 2.3846153846153846, - "tolerance": 0.11923076923076924 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_17.json b/sleep_assessment/cases/case10/case10_17.json deleted file mode 100644 index a56412bd2f5c5d5c2b1619f922cd9110bd3d0c01..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_17.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 19, - "tolerance": 0.9500000000000001 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 4.052631578947368, - "tolerance": 0.2026315789473684 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_18.json b/sleep_assessment/cases/case10/case10_18.json deleted file mode 100644 index 016b7942d06ea3f5b2f0c0373507f40cb408e491..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_18.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 19, - "tolerance": 0.9500000000000001 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 2.8421052631578947, - "tolerance": 0.14210526315789473 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_19.json b/sleep_assessment/cases/case10/case10_19.json deleted file mode 100644 index 3f27eb812985aa40338adbb307d95c1b7fc3256c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_19.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 10, - "tolerance": 0.5 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 2.65, - "tolerance": 0.1325 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_20.json b/sleep_assessment/cases/case10/case10_20.json deleted file mode 100644 index 3f9a7f8f6e9e53d88aab2277fd841f988cfde6c8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_20.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 6, - "tolerance": 0.30000000000000004 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 15.833333333333334, - "tolerance": 0.7916666666666667 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_21.json b/sleep_assessment/cases/case10/case10_21.json deleted file mode 100644 index 06a89c79ce06208718f0662ece5844a856408597..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_21.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0.2 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 8.875, - "tolerance": 0.44375000000000003 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_22.json b/sleep_assessment/cases/case10/case10_22.json deleted file mode 100644 index 149657f5d9eff318b5d31ef9530c634aed26368f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_22.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0.2 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 2.625, - "tolerance": 0.13125 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_23.json b/sleep_assessment/cases/case10/case10_23.json deleted file mode 100644 index 161b62893921c70b2b47a0119d3527050338bd12..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_23.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0.2 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 6.5, - "tolerance": 0.325 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_24.json b/sleep_assessment/cases/case10/case10_24.json deleted file mode 100644 index a2a92ad05b806b541be6bf1d29c9ceccadcacc80..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_24.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 19, - "tolerance": 0.9500000000000001 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 4.342105263157895, - "tolerance": 0.21710526315789475 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case10/case10_25.json b/sleep_assessment/cases/case10/case10_25.json deleted file mode 100644 index 5fd90a6a9c9cd79cae01aa791d8b3fd63a94e01b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case10/case10_25.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two N3-related sleep metrics for the whole night: the number of N3 bouts, and the mean N3 bout length in minutes. Here, a bout is defined as a contiguous N3 segment with length greater than or equal to 2 epochs. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) the number of N3 bouts across the night\n2) the mean N3 bout length in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"n3_bout_count\" and \"n3_mean_bout_length_min\".\n5. \"n3_bout_count\" must be an INTEGER or null.\n6. \"n3_mean_bout_length_min\" must be a FLOAT or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"n3_bout_count\": , \"n3_mean_bout_length_min\": }", - "metrics": [ - { - "metric_id": "n3_bout_count_accuracy", - "type": "numeric_check", - "target_key": "n3_bout_count", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0.2 - } - }, - { - "metric_id": "n3_mean_bout_length_accuracy", - "type": "numeric_check", - "target_key": "n3_mean_bout_length_min", - "weight": 50, - "params": { - "gt_value": 12.0, - "tolerance": 0.6000000000000001 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_01.json b/sleep_assessment/cases/case11/case11_01.json deleted file mode 100644 index a5777f3bcb8d17f2c891fb8568849108af253d0e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_01.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 353.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 391.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "high" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_02.json b/sleep_assessment/cases/case11/case11_02.json deleted file mode 100644 index 9363519f89642b8b25a69bc588cfcbc97262eb40..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_02.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 4.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 32.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_03.json b/sleep_assessment/cases/case11/case11_03.json deleted file mode 100644 index d5640803adb601e1e7ec3d1a95bef8637688d108..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_03.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 241.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 256.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_04.json b/sleep_assessment/cases/case11/case11_04.json deleted file mode 100644 index 5c857d315ac94ba5c1a029f73578ff426dbcf658..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_04.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 256.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 258.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "low" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_05.json b/sleep_assessment/cases/case11/case11_05.json deleted file mode 100644 index d5c0440d3e1852bd0f8911f37ee4e44c333ba4e0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_05.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 21.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 33.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_06.json b/sleep_assessment/cases/case11/case11_06.json deleted file mode 100644 index 225eb75b559fbafc25b685d583a8b61c3ae9907b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_06.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 154.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 196.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "high" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_07.json b/sleep_assessment/cases/case11/case11_07.json deleted file mode 100644 index 89f7897a1b8c075d5751d58742e72ba210cef1a9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_07.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 397.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 405.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_08.json b/sleep_assessment/cases/case11/case11_08.json deleted file mode 100644 index 633498c1b341201221b6fda6ca427450a28a57d4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_08.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 148.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 195.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "high" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_09.json b/sleep_assessment/cases/case11/case11_09.json deleted file mode 100644 index 736d0865ce9de740f93011e371f3d288babb8301..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_09.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 493.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 500.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_10.json b/sleep_assessment/cases/case11/case11_10.json deleted file mode 100644 index 6a9f93e547d1a7820dd1be53cd537cad6735e6df..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_10.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 112.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 122.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_11.json b/sleep_assessment/cases/case11/case11_11.json deleted file mode 100644 index 6bd51f1cc7a31b1dbb23f6031a1087d2c494b60a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_11.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 47.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 56.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_12.json b/sleep_assessment/cases/case11/case11_12.json deleted file mode 100644 index ecc65e8e161e8f17f6d07c1a034e643c59594f72..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_12.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 56.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 66.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_13.json b/sleep_assessment/cases/case11/case11_13.json deleted file mode 100644 index 30192a129dd58532e7aa1b03b770adb833cf51b4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_13.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 282.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 301.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_14.json b/sleep_assessment/cases/case11/case11_14.json deleted file mode 100644 index 0b0012884b15a42e123372a63700ea081ca16ab1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_14.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 50.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 56.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_15.json b/sleep_assessment/cases/case11/case11_15.json deleted file mode 100644 index 4b1cd15a3a8a8c306ea41d7acd5a4ed9652f8294..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_15.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 327.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 346.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_16.json b/sleep_assessment/cases/case11/case11_16.json deleted file mode 100644 index a00cd932e8df970150d487788b0a291fd44dfab2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_16.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 410.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 422.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_17.json b/sleep_assessment/cases/case11/case11_17.json deleted file mode 100644 index 1edf643a846743a1f65e21c882402bc455a96a9d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_17.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 486.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 494.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_18.json b/sleep_assessment/cases/case11/case11_18.json deleted file mode 100644 index 7e3e319af21ae40a99ba3708c514067f37ff6571..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_18.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 400.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 405.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "low" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_19.json b/sleep_assessment/cases/case11/case11_19.json deleted file mode 100644 index b2811637d9f2a248fe82e05f35d4fd7eb6079840..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_19.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 174.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 174.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "low" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_20.json b/sleep_assessment/cases/case11/case11_20.json deleted file mode 100644 index f43b324e9f2b28aa6bd15fe29ddfcd6ceb46b7e9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_20.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 385.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 390.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_21.json b/sleep_assessment/cases/case11/case11_21.json deleted file mode 100644 index 67c0225b3292232d85280b6ad6ee797461228fcc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_21.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 270.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 353.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "high" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_22.json b/sleep_assessment/cases/case11/case11_22.json deleted file mode 100644 index 8f651cf62398a26265d275a900e93ecbdf18eeea..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_22.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 81.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 107.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_23.json b/sleep_assessment/cases/case11/case11_23.json deleted file mode 100644 index 85d5c721c8f89ca8229da53693cf1dd921b841ed..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_23.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 131.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 254.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "high" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_24.json b/sleep_assessment/cases/case11/case11_24.json deleted file mode 100644 index 6417e0d99f6846ebde677e9fdf30eb908e50e4e9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_24.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 265.5, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 281.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case11/case11_25.json b/sleep_assessment/cases/case11/case11_25.json deleted file mode 100644 index d73fd0aa844fd22336fa55d14f6b021b8ea131f4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case11/case11_25.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to locate the longest contiguous Wake (W) segment during the nocturnal sleep period. Define the nocturnal sleep period as the interval from the first non-Wake sleep epoch to the last non-Wake sleep epoch, excluding Wake epochs before sleep onset and after final awakening. Report the start time and end time of this longest Wake segment in minutes, using the first non-Wake sleep epoch as 0 minutes. Then classify the impact of this single longest awakening interruption on sleep continuity as low, medium, or high: low for <5 minutes, medium for 5 to <30 minutes, and high for >=30 minutes. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) the start time in minutes of the longest contiguous Wake (W) segment during the sleep period\n2) the end time in minutes of that longest contiguous Wake (W) segment\n3) the sleep-continuity impact level of that Wake interruption\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"longest_w_start_min\", \"longest_w_end_min\", and \"wake_interruption_impact\".\n5. \"longest_w_start_min\" and \"longest_w_end_min\" must be FLOATS in minutes or null.\n6. \"wake_interruption_impact\" must be exactly one of \"low\", \"medium\", \"high\", or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_w_start_min\": , \"longest_w_end_min\": , \"wake_interruption_impact\": <\"low\"|\"medium\"|\"high\"|null>}", - "metrics": [ - { - "metric_id": "longest_w_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_start_min", - "weight": 30, - "params": { - "gt_value": 204.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "longest_w_end_time_accuracy", - "type": "numeric_check", - "target_key": "longest_w_end_min", - "weight": 30, - "params": { - "gt_value": 231.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "wake_interruption_impact_accuracy", - "type": "categorical_check", - "target_key": "wake_interruption_impact", - "weight": 40, - "params": { - "gt_value": "medium" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_01.json b/sleep_assessment/cases/case12/case12_01.json deleted file mode 100644 index e974afb4ed7d7a0efed4a8f4cd8bc85c63788f19..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_01.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 70.99567099567099, - "tolerance": 3.54978354978355 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 45.76271186440678, - "tolerance": 2.288135593220339 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The first-half N3 ratio is 70.995671% of total N3, the second-half REM ratio is 45.762712% of total REM, total N3 duration is 115.500000 minutes, and total REM duration is 59.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_02.json b/sleep_assessment/cases/case12/case12_02.json deleted file mode 100644 index 677c407e317e619e1fa8d56d1533e9eb15eacd1f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_02.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 52.22929936305732, - "tolerance": 2.611464968152866 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 77.45098039215686, - "tolerance": 3.8725490196078436 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The first-half N3 ratio is 52.229299% of total N3, the second-half REM ratio is 77.450980% of total REM, total N3 duration is 78.500000 minutes, and total REM duration is 51.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_03.json b/sleep_assessment/cases/case12/case12_03.json deleted file mode 100644 index 9bd757e1d5dcb968b62c4086247c06238c33dfd2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_03.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 72.83236994219654, - "tolerance": 3.641618497109827 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 84.14096916299559, - "tolerance": 4.20704845814978 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The first-half N3 ratio is 72.832370% of total N3, the second-half REM ratio is 84.140969% of total REM, total N3 duration is 86.500000 minutes, and total REM duration is 113.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_04.json b/sleep_assessment/cases/case12/case12_04.json deleted file mode 100644 index e88b50a7dcaa76b727bda0fb6aa6b7074cc6a016..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_04.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 85.04672897196262, - "tolerance": 4.252336448598132 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 60.43478260869565, - "tolerance": 3.0217391304347827 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. The first-half N3 ratio is 85.046729% of total N3, the second-half REM ratio is 60.434783% of total REM, total N3 duration is 107.000000 minutes, and total REM duration is 115.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_05.json b/sleep_assessment/cases/case12/case12_05.json deleted file mode 100644 index bfc7d8f9ca32588193b2d070c69bd973b72135e0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_05.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 60.36585365853659, - "tolerance": 3.0182926829268295 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 95.23809523809524, - "tolerance": 4.761904761904762 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The first-half N3 ratio is 60.365854% of total N3, the second-half REM ratio is 95.238095% of total REM, total N3 duration is 82.000000 minutes, and total REM duration is 21.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_06.json b/sleep_assessment/cases/case12/case12_06.json deleted file mode 100644 index 3b477acf25d8797cf1ba1b39ffe27df5c38db0a9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_06.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 56.52173913043478, - "tolerance": 2.8260869565217392 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 81.56028368794327, - "tolerance": 4.078014184397164 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_01.edf. The first-half N3 ratio is 56.521739% of total N3, the second-half REM ratio is 81.560284% of total REM, total N3 duration is 11.500000 minutes, and total REM duration is 70.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that total N3 is limited or short, and that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_07.json b/sleep_assessment/cases/case12/case12_07.json deleted file mode 100644 index 80b9a6d6aaa2e9e2d22f9933439135392ef44ba7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_07.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 68.30357142857143, - "tolerance": 3.4151785714285716 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 58.04597701149425, - "tolerance": 2.902298850574713 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_02.edf. The first-half N3 ratio is 68.303571% of total N3, the second-half REM ratio is 58.045977% of total REM, total N3 duration is 112.000000 minutes, and total REM duration is 87.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_08.json b/sleep_assessment/cases/case12/case12_08.json deleted file mode 100644 index 103d42d3d9b6026189dcc43a1b0bdb20e5451ca6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_08.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 66.0, - "tolerance": 3.3000000000000003 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 100.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_03.edf. The first-half N3 ratio is 66.000000% of total N3, the second-half REM ratio is 100.000000% of total REM, total N3 duration is 75.000000 minutes, and total REM duration is 76.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_09.json b/sleep_assessment/cases/case12/case12_09.json deleted file mode 100644 index b4e2113186b22f1e20b16ac5c6b534bbc504e15c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_09.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 100.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 52.22672064777328, - "tolerance": 2.611336032388664 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_04.edf. The first-half N3 ratio is 100.000000% of total N3, the second-half REM ratio is 52.226721% of total REM, total N3 duration is 32.000000 minutes, and total REM duration is 123.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_10.json b/sleep_assessment/cases/case12/case12_10.json deleted file mode 100644 index 8f9dba7f5a07b1e1747616f9b14f24f73a4698cc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_10.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 72.48322147651007, - "tolerance": 3.6241610738255035 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 57.48031496062992, - "tolerance": 2.8740157480314963 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_05.edf. The first-half N3 ratio is 72.483221% of total N3, the second-half REM ratio is 57.480315% of total REM, total N3 duration is 74.500000 minutes, and total REM duration is 63.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_11.json b/sleep_assessment/cases/case12/case12_11.json deleted file mode 100644 index 091b7689ea9d2b6c680b6f8971f1ba4ba7332373..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_11.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 97.93103448275862, - "tolerance": 4.8965517241379315 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 51.96078431372549, - "tolerance": 2.5980392156862746 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The first-half N3 ratio is 97.931034% of total N3, the second-half REM ratio is 51.960784% of total REM, total N3 duration is 72.500000 minutes, and total REM duration is 51.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_12.json b/sleep_assessment/cases/case12/case12_12.json deleted file mode 100644 index 69350db2ed621f076ce118e9b97aeea2695fad23..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_12.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 100.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The first-half N3 ratio is 100.000000% of total N3, the second-half REM ratio is 0.000000% of total REM, total N3 duration is 30.500000 minutes, and total REM duration is 30.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_13.json b/sleep_assessment/cases/case12/case12_13.json deleted file mode 100644 index 88db810a488be830f272f553440b2bde3f9eaff6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_13.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 57.00325732899023, - "tolerance": 2.850162866449512 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 81.3953488372093, - "tolerance": 4.069767441860465 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The first-half N3 ratio is 57.003257% of total N3, the second-half REM ratio is 81.395349% of total REM, total N3 duration is 153.500000 minutes, and total REM duration is 43.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_14.json b/sleep_assessment/cases/case12/case12_14.json deleted file mode 100644 index d9694d76b8f39d9185207a8cd06df99f3a895bee..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_14.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 65.39682539682539, - "tolerance": 3.2698412698412698 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 56.52173913043478, - "tolerance": 2.8260869565217392 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The first-half N3 ratio is 65.396825% of total N3, the second-half REM ratio is 56.521739% of total REM, total N3 duration is 157.500000 minutes, and total REM duration is 46.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_15.json b/sleep_assessment/cases/case12/case12_15.json deleted file mode 100644 index 0fbd49990dfd535b285d8dbf7a35c989130cedc4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_15.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 100.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 65.93406593406593, - "tolerance": 3.2967032967032965 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The first-half N3 ratio is 100.000000% of total N3, the second-half REM ratio is 65.934066% of total REM, total N3 duration is 49.500000 minutes, and total REM duration is 45.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_16.json b/sleep_assessment/cases/case12/case12_16.json deleted file mode 100644 index 21ac88832cc34902d25843866348169396ddead5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_16.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 85.61643835616438, - "tolerance": 4.280821917808219 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 64.64088397790056, - "tolerance": 3.232044198895028 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_01.edf. The first-half N3 ratio is 85.616438% of total N3, the second-half REM ratio is 64.640884% of total REM, total N3 duration is 73.000000 minutes, and total REM duration is 90.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_17.json b/sleep_assessment/cases/case12/case12_17.json deleted file mode 100644 index 503a4b8a6d717e37a10b7aae99ad6e5a282b7bdd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_17.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 89.65517241379311, - "tolerance": 4.482758620689656 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 54.54545454545455, - "tolerance": 2.7272727272727275 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_02.edf. The first-half N3 ratio is 89.655172% of total N3, the second-half REM ratio is 54.545455% of total REM, total N3 duration is 87.000000 minutes, and total REM duration is 121.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_18.json b/sleep_assessment/cases/case12/case12_18.json deleted file mode 100644 index 3ddd0374148d1fb4ff1ef7e89453084a135ce82e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_18.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 84.6774193548387, - "tolerance": 4.233870967741935 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 66.04477611940298, - "tolerance": 3.3022388059701493 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_03.edf. The first-half N3 ratio is 84.677419% of total N3, the second-half REM ratio is 66.044776% of total REM, total N3 duration is 62.000000 minutes, and total REM duration is 134.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_19.json b/sleep_assessment/cases/case12/case12_19.json deleted file mode 100644 index a3ef726cab9570ab8286e0161565fd1baaa96bfb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_19.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 100.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 70.61224489795919, - "tolerance": 3.5306122448979593 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_04.edf. The first-half N3 ratio is 100.000000% of total N3, the second-half REM ratio is 70.612245% of total REM, total N3 duration is 32.000000 minutes, and total REM duration is 122.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_20.json b/sleep_assessment/cases/case12/case12_20.json deleted file mode 100644 index 8457d907f568dfe15391110f2c4f862d24160a9a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_20.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 97.43589743589743, - "tolerance": 4.871794871794872 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 80.50541516245487, - "tolerance": 4.025270758122743 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_05.edf. The first-half N3 ratio is 97.435897% of total N3, the second-half REM ratio is 80.505415% of total REM, total N3 duration is 97.500000 minutes, and total REM duration is 138.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_21.json b/sleep_assessment/cases/case12/case12_21.json deleted file mode 100644 index 1d619ee321dcf986a71ee47a187c9dfb47b95e51..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_21.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 100.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 46.91358024691358, - "tolerance": 2.345679012345679 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_01.edf. The first-half N3 ratio is 100.000000% of total N3, the second-half REM ratio is 46.913580% of total REM, total N3 duration is 36.000000 minutes, and total REM duration is 40.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_22.json b/sleep_assessment/cases/case12/case12_22.json deleted file mode 100644 index 6f0cf0397e4303983da0d8d54a3d1c2e6d1869b7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_22.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 100.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_02.edf. The first-half N3 ratio is 0.000000% of total N3, the second-half REM ratio is 100.000000% of total REM, total N3 duration is 10.500000 minutes, and total REM duration is 0.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that total N3 is limited or short, and that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_23.json b/sleep_assessment/cases/case12/case12_23.json deleted file mode 100644 index d79fb70c83f7157893cc541ee301c507905b6619..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_23.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 90.3225806451613, - "tolerance": 4.516129032258065 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_03.edf. The first-half N3 ratio is 90.322581% of total N3, the second-half REM ratio is 0.000000% of total REM, total N3 duration is 31.000000 minutes, and total REM duration is 0.500000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_24.json b/sleep_assessment/cases/case12/case12_24.json deleted file mode 100644 index 23f47adb2b0067e699d1eac99314803b90d7a5b1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_24.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 58.72093023255814, - "tolerance": 2.9360465116279073 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 46.666666666666664, - "tolerance": 2.3333333333333335 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_04.edf. The first-half N3 ratio is 58.720930% of total N3, the second-half REM ratio is 46.666667% of total REM, total N3 duration is 86.000000 minutes, and total REM duration is 45.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is not enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case12/case12_25.json b/sleep_assessment/cases/case12/case12_25.json deleted file mode 100644 index 45a05ae2aa1fe77f3bbdcc292910689baa17913c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case12/case12_25.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze half-night N3 and REM patterns. Split the entire label file into a first half and a second half using the midpoint of the label sequence. Calculate the percentage of total N3 sleep that occurs in the first half of the label file, and the percentage of total REM sleep that occurs in the second half of the label file. Then determine whether deep sleep is concentrated in the first half of the night, defined as first-half N3 being at least 70% of total N3, and whether REM is enhanced in the second half of the night, defined as second-half REM being at least 60% of total REM. If total N3 duration is less than 30 minutes, explicitly state that N3 is limited or short before interpreting whether the limited N3 is mainly concentrated in the first half. Please clearly report both percentage values and the two final interpretations." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two percentage values from the agent's report:\n1) the percentage of total N3 sleep that occurs in the first half of the night\n2) the percentage of total REM sleep that occurs in the second half of the night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_n3_ratio_percent\" and \"second_half_rem_ratio_percent\".\n5. Both values must be FLOATS or null.\n6. Values are percentages; if the report says 72.5%, output 72.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_n3_ratio_percent\": , \"second_half_rem_ratio_percent\": }", - "metrics": [ - { - "metric_id": "first_half_n3_ratio_accuracy", - "type": "numeric_check", - "target_key": "first_half_n3_ratio_percent", - "weight": 30, - "params": { - "gt_value": 25.252525252525253, - "tolerance": 1.2626262626262628 - } - }, - { - "metric_id": "second_half_rem_ratio_accuracy", - "type": "numeric_check", - "target_key": "second_half_rem_ratio_percent", - "weight": 30, - "params": { - "gt_value": 87.25490196078431, - "tolerance": 4.362745098039215 - } - }, - { - "metric_id": "half_night_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_05.edf. The first-half N3 ratio is 25.252525% of total N3, the second-half REM ratio is 87.254902% of total REM, total N3 duration is 49.500000 minutes, and total REM duration is 51.000000 minutes. The benchmark rules are: first-half deep-sleep concentration is true only when first-half N3 is >=70% of total N3; second-half REM enhancement is true only when second-half REM is >=60% of total REM; total N3 <30 minutes is a short-N3 edge case. The correct response must say that deep sleep is not concentrated in the first half of the night. The correct response must say that REM is enhanced or increased in the second half of the night. Mark the response as correct only if it gets both interpretations right and also handles any short-N3, absent-N3, or absent-REM edge case described above. If the response gives the opposite conclusion, ignores a required short-stage caveat, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_01.json b/sleep_assessment/cases/case13/case13_01.json deleted file mode 100644 index 13b2fd1e08c62d10e97d534beffa7637c6871a11..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_01.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 20.454545454545453, - "tolerance": 1.0227272727272727 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_02.json b/sleep_assessment/cases/case13/case13_02.json deleted file mode 100644 index 313cb53408bf21072a93f2efe97d0f4122fda9ed..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_02.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 24.86787204450626, - "tolerance": 1.2433936022253131 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_03.json b/sleep_assessment/cases/case13/case13_03.json deleted file mode 100644 index 85e14a74203c3a7fce94c0cd42842870fc5159db..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_03.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 19.827373612823674, - "tolerance": 0.9913686806411838 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_04.json b/sleep_assessment/cases/case13/case13_04.json deleted file mode 100644 index 2f8c60392cbf1edbd3e1cdf2fa73dc74d856f8fa..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_04.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 12.834224598930481, - "tolerance": 0.6417112299465241 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_05.json b/sleep_assessment/cases/case13/case13_05.json deleted file mode 100644 index a915d35193d63a8aa0a3dd06273700db9477be78..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_05.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 20.932642487046632, - "tolerance": 1.0466321243523315 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_06.json b/sleep_assessment/cases/case13/case13_06.json deleted file mode 100644 index 78f886bd3980311e97a24d9ef82e6262d7be73ee..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_06.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 14.33854907539118, - "tolerance": 0.716927453769559 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_07.json b/sleep_assessment/cases/case13/case13_07.json deleted file mode 100644 index acd632b866e5c697968767053af80ff0b7c5a658..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_07.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 13.7984496124031, - "tolerance": 0.689922480620155 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_08.json b/sleep_assessment/cases/case13/case13_08.json deleted file mode 100644 index 63de99e2129a1080a529c9ac717e5e18da48d80a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_08.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 25.263157894736842, - "tolerance": 1.2631578947368423 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_09.json b/sleep_assessment/cases/case13/case13_09.json deleted file mode 100644 index 8029c6edcf5ee01b2a0c3dc13cf78457603aaacc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_09.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 13.461538461538462, - "tolerance": 0.6730769230769231 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_10.json b/sleep_assessment/cases/case13/case13_10.json deleted file mode 100644 index 112242fd0b6182ef8c037a2385a451197a178782..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_10.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 32.13720316622691, - "tolerance": 1.6068601583113455 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_11.json b/sleep_assessment/cases/case13/case13_11.json deleted file mode 100644 index c06d7ff056cc1c6949bc6bf69b65a5af63d9c8ee..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_11.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 21.091877496671103, - "tolerance": 1.0545938748335553 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_12.json b/sleep_assessment/cases/case13/case13_12.json deleted file mode 100644 index bdcb56ef85b086a83964527343422fd189b10245..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_12.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 26.373626373626376, - "tolerance": 1.3186813186813189 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_13.json b/sleep_assessment/cases/case13/case13_13.json deleted file mode 100644 index 9aca30b37ff7733188de3cf67d69c775cf1a3ac7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_13.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 18.410041841004187, - "tolerance": 0.9205020920502094 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_14.json b/sleep_assessment/cases/case13/case13_14.json deleted file mode 100644 index c08f4230721c4ae6fa710838db008c9802c61105..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_14.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 22.524916943521596, - "tolerance": 1.12624584717608 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_15.json b/sleep_assessment/cases/case13/case13_15.json deleted file mode 100644 index 1537dcd668312fb6428b2a0d04a10ba35866b95c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_15.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 15.567567567567567, - "tolerance": 0.7783783783783784 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_16.json b/sleep_assessment/cases/case13/case13_16.json deleted file mode 100644 index 9ea0e11d6d3c8cd59deee1b7ee4fec47b5405fde..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_16.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 19.808306709265175, - "tolerance": 0.9904153354632588 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_17.json b/sleep_assessment/cases/case13/case13_17.json deleted file mode 100644 index 7175cb4678fcb3ff150b11ce599df3d803de0de0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_17.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 20.48167539267016, - "tolerance": 1.024083769633508 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_18.json b/sleep_assessment/cases/case13/case13_18.json deleted file mode 100644 index 2fc1c6816335739b49be6f20886a20a7401903d3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_18.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 16.478405315614616, - "tolerance": 0.8239202657807309 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_19.json b/sleep_assessment/cases/case13/case13_19.json deleted file mode 100644 index c92b5cafaa03abbeff9dbbeb524feffc51344e50..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_19.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 12.25305216426193, - "tolerance": 0.6126526082130965 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_20.json b/sleep_assessment/cases/case13/case13_20.json deleted file mode 100644 index be4cd64a8f2c154abac9baf80c0c2415ce8ee1e7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_20.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 10.984251968503937, - "tolerance": 0.5492125984251969 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_21.json b/sleep_assessment/cases/case13/case13_21.json deleted file mode 100644 index 5a0646d2564a9a398f8b321377eb0e6eed018c9b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_21.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 17.573221757322177, - "tolerance": 0.8786610878661089 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_22.json b/sleep_assessment/cases/case13/case13_22.json deleted file mode 100644 index 44cb8f384d03068ae295f24cfbf919e1105782bd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_22.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 18.0, - "tolerance": 0.9 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_23.json b/sleep_assessment/cases/case13/case13_23.json deleted file mode 100644 index 60bc6b090b459ed50c159611b168f5ef722b17db..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_23.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 18.849557522123895, - "tolerance": 0.9424778761061948 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_24.json b/sleep_assessment/cases/case13/case13_24.json deleted file mode 100644 index 1a76d957fc5f5093d151ebd45654a141d30495c7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_24.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 20.91743119266055, - "tolerance": 1.0458715596330277 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case13/case13_25.json b/sleep_assessment/cases/case13/case13_25.json deleted file mode 100644 index 7111aefc72ddcb73a63f41e561a778374e9797d7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case13/case13_25.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the Sleep Fragmentation Index (SFI). Define the sleep period as the interval from the first non-Wake epoch to the last non-Wake epoch. Within this sleep period, count the number of awakenings as the number of contiguous Wake (W) bouts, and count sleep-stage shifts as the number of adjacent epoch stage changes where both adjacent epochs are non-Wake sleep stages (N1, N2, N3, or R/REM). Calculate total sleep time as the number of non-Wake epochs multiplied by 30 seconds. SFI is defined as (number of awakenings + sleep-stage shifts) divided by total sleep time in hours, reported as events per hour of total sleep time. Please clearly report the final SFI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the Sleep Fragmentation Index (SFI) from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"sleep_fragmentation_index\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"sleep_fragmentation_index\": null}.\n\n### OUTPUT TEMPLATE\n{\"sleep_fragmentation_index\": }", - "metrics": [ - { - "metric_id": "sleep_fragmentation_index_accuracy", - "type": "numeric_check", - "target_key": "sleep_fragmentation_index", - "weight": 100, - "params": { - "gt_value": 10.4, - "tolerance": 0.52 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case14/case14_01.json b/sleep_assessment/cases/case14/case14_01.json deleted file mode 100644 index f44328a0873dc7b8bd1cfbbdd60811c758af715b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_01.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 30.0, - "tolerance": 1.5 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 8.295454545454545, - "tolerance": 0.4147727272727273 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 22.045454545454547, - "tolerance": 1.1022727272727273 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 26.25, - "tolerance": 1.3125 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 13.409090909090908, - "tolerance": 0.6704545454545454 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_02.json b/sleep_assessment/cases/case14/case14_02.json deleted file mode 100644 index 373c83459029f60748a8fc89377b374cd4572b64..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_02.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 25.414937759336098, - "tolerance": 1.270746887966805 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 11.929460580912863, - "tolerance": 0.5964730290456431 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 35.788381742738586, - "tolerance": 1.7894190871369293 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 16.286307053941908, - "tolerance": 0.8143153526970954 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 10.58091286307054, - "tolerance": 0.529045643153527 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_03.json b/sleep_assessment/cases/case14/case14_03.json deleted file mode 100644 index ab79d9c29ba4a0c45ec1701c1030381752c14225..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_03.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 13.997879109225876, - "tolerance": 0.6998939554612938 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 17.497348886532343, - "tolerance": 0.8748674443266172 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 26.08695652173913, - "tolerance": 1.3043478260869565 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 18.345705196182397, - "tolerance": 0.9172852598091199 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 24.072110286320253, - "tolerance": 1.2036055143160127 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_04.json b/sleep_assessment/cases/case14/case14_04.json deleted file mode 100644 index 734082a0812ac9d77a78b0aad7f00a475204cdd9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_04.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 2.907580477673936, - "tolerance": 0.1453790238836968 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 6.749740394600208, - "tolerance": 0.3374870197300104 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 44.23676012461059, - "tolerance": 2.2118380062305296 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 22.22222222222222, - "tolerance": 1.1111111111111112 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 23.883696780893043, - "tolerance": 1.1941848390446521 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_05.json b/sleep_assessment/cases/case14/case14_05.json deleted file mode 100644 index e98487e90ba9cb78a09275ebe53a48fc9c97570d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_05.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 33.82857142857143, - "tolerance": 1.6914285714285715 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 12.342857142857143, - "tolerance": 0.6171428571428572 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 30.285714285714285, - "tolerance": 1.5142857142857142 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 18.742857142857144, - "tolerance": 0.9371428571428573 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 4.8, - "tolerance": 0.24 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_06.json b/sleep_assessment/cases/case14/case14_06.json deleted file mode 100644 index b4ea47329b79a82f2fb71ab96b5a6d0a3ce8a219..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_06.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 17.681498829039814, - "tolerance": 0.8840749414519907 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 12.763466042154567, - "tolerance": 0.6381733021077284 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 50.35128805620609, - "tolerance": 2.5175644028103044 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 2.693208430913349, - "tolerance": 0.13466042154566746 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 16.510538641686182, - "tolerance": 0.8255269320843092 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_07.json b/sleep_assessment/cases/case14/case14_07.json deleted file mode 100644 index 9c4520360ec3948e0b08ba048b39bbb3155554ff..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_07.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 9.57943925233645, - "tolerance": 0.4789719626168225 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 7.827102803738318, - "tolerance": 0.3913551401869159 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 36.098130841121495, - "tolerance": 1.8049065420560748 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 26.16822429906542, - "tolerance": 1.3084112149532712 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 20.327102803738317, - "tolerance": 1.016355140186916 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_08.json b/sleep_assessment/cases/case14/case14_08.json deleted file mode 100644 index 9e34014d8182cde7ad4d573633b4c3c17fdf7d4a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_08.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 42.24318658280922, - "tolerance": 2.1121593291404612 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 8.49056603773585, - "tolerance": 0.42452830188679247 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 17.61006289308176, - "tolerance": 0.8805031446540881 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 15.723270440251572, - "tolerance": 0.7861635220125787 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 15.932914046121594, - "tolerance": 0.7966457023060798 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_09.json b/sleep_assessment/cases/case14/case14_09.json deleted file mode 100644 index f27127f7c272d56d734e6d2eb116adb0eeccfe4d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_09.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 7.874015748031496, - "tolerance": 0.3937007874015748 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 8.956692913385826, - "tolerance": 0.4478346456692913 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 52.55905511811024, - "tolerance": 2.627952755905512 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 6.299212598425197, - "tolerance": 0.3149606299212599 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 24.311023622047244, - "tolerance": 1.2155511811023623 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_10.json b/sleep_assessment/cases/case14/case14_10.json deleted file mode 100644 index 756b1038d8ec4147c217be0a4857bd9150dba6f0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_10.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 20.959332638164756, - "tolerance": 1.047966631908238 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 14.70281543274244, - "tolerance": 0.7351407716371221 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 35.557872784150156, - "tolerance": 1.777893639207508 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 15.537017726798748, - "tolerance": 0.7768508863399375 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 13.2429614181439, - "tolerance": 0.662148070907195 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_11.json b/sleep_assessment/cases/case14/case14_11.json deleted file mode 100644 index 77d4894d6bd6cb9d83b6b712e6464217e0257600..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_11.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 30.719557195571955, - "tolerance": 1.5359778597785978 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 4.3357933579335795, - "tolerance": 0.21678966789667897 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 42.15867158671587, - "tolerance": 2.1079335793357936 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 13.376383763837639, - "tolerance": 0.668819188191882 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 9.409594095940959, - "tolerance": 0.4704797047970479 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_12.json b/sleep_assessment/cases/case14/case14_12.json deleted file mode 100644 index 4eac2169761dd0ec25f73861ca76d035c53f9c26..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_12.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 66.26506024096386, - "tolerance": 3.313253012048193 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 0.27803521779425394, - "tolerance": 0.013901760889712698 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 22.150139017608897, - "tolerance": 1.107506950880445 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 5.653382761816497, - "tolerance": 0.28266913809082483 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 5.653382761816497, - "tolerance": 0.28266913809082483 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_13.json b/sleep_assessment/cases/case14/case14_13.json deleted file mode 100644 index 52da55d2de66cada8fc7ffb21888dd1bfa766ba6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_13.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 31.649189704480456, - "tolerance": 1.5824594852240228 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 3.336510962821735, - "tolerance": 0.16682554814108674 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 27.550047664442324, - "tolerance": 1.3775023832221163 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 29.26596758817922, - "tolerance": 1.4632983794089611 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 8.198284080076263, - "tolerance": 0.4099142040038132 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_14.json b/sleep_assessment/cases/case14/case14_14.json deleted file mode 100644 index 238cbe0eac2a0086b9de817b017e8be120aa438b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_14.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 31.2, - "tolerance": 1.56 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 2.057142857142857, - "tolerance": 0.10285714285714286 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 20.228571428571428, - "tolerance": 1.0114285714285713 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 36.0, - "tolerance": 1.8 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 10.514285714285714, - "tolerance": 0.5257142857142857 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_15.json b/sleep_assessment/cases/case14/case14_15.json deleted file mode 100644 index 49b096095bf940e113b98ed220610660d3319794..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_15.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 31.73431734317343, - "tolerance": 1.5867158671586716 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 3.874538745387454, - "tolerance": 0.1937269372693727 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 46.86346863468635, - "tolerance": 2.3431734317343174 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 9.132841328413285, - "tolerance": 0.4566420664206643 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 8.394833948339484, - "tolerance": 0.4197416974169742 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_16.json b/sleep_assessment/cases/case14/case14_16.json deleted file mode 100644 index d90bbf5e1898c28e74f402b8f7b19417f7f47f4f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_16.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 6.845238095238095, - "tolerance": 0.34226190476190477 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 7.341269841269841, - "tolerance": 0.3670634920634921 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 53.37301587301587, - "tolerance": 2.668650793650794 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 14.484126984126984, - "tolerance": 0.7242063492063493 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 17.956349206349206, - "tolerance": 0.8978174603174603 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_17.json b/sleep_assessment/cases/case14/case14_17.json deleted file mode 100644 index 00ac722e914bc86bb85f5a9f7c15a94419e85a40..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_17.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 10.914179104477611, - "tolerance": 0.5457089552238806 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 5.7835820895522385, - "tolerance": 0.2891791044776119 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 44.49626865671642, - "tolerance": 2.224813432835821 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 16.23134328358209, - "tolerance": 0.8115671641791045 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 22.574626865671643, - "tolerance": 1.1287313432835822 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_18.json b/sleep_assessment/cases/case14/case14_18.json deleted file mode 100644 index 487c2b431efd7eb61edb56a0e231fc3230f0ec17..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_18.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 5.445026178010472, - "tolerance": 0.27225130890052357 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 5.968586387434555, - "tolerance": 0.29842931937172773 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 47.539267015706805, - "tolerance": 2.3769633507853403 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 12.984293193717278, - "tolerance": 0.6492146596858639 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 28.06282722513089, - "tolerance": 1.4031413612565444 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_19.json b/sleep_assessment/cases/case14/case14_19.json deleted file mode 100644 index 36fa3c76cd3335b8b6ca6a4f9f8159427fa0525b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_19.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 1.206140350877193, - "tolerance": 0.060307017543859656 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 5.2631578947368425, - "tolerance": 0.26315789473684215 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 59.64912280701754, - "tolerance": 2.982456140350877 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 7.017543859649122, - "tolerance": 0.3508771929824561 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 26.864035087719298, - "tolerance": 1.343201754385965 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_20.json b/sleep_assessment/cases/case14/case14_20.json deleted file mode 100644 index fa95e14ae55fb8a73301b891c8cf42b0f29b4b44..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_20.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 4.9579045837231055, - "tolerance": 0.24789522918615528 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 7.015902712815715, - "tolerance": 0.3507951356407858 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 43.87277829747428, - "tolerance": 2.193638914873714 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 18.24134705332086, - "tolerance": 0.9120673526660431 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 25.912067352666043, - "tolerance": 1.2956033676333023 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_21.json b/sleep_assessment/cases/case14/case14_21.json deleted file mode 100644 index 39d3ce5c95d38b104cd56a5f5568e9c1f2ef6806..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_21.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 43.43195266272189, - "tolerance": 2.1715976331360944 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 6.863905325443787, - "tolerance": 0.3431952662721894 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 31.597633136094675, - "tolerance": 1.5798816568047338 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 8.520710059171599, - "tolerance": 0.42603550295857995 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 9.585798816568047, - "tolerance": 0.47928994082840237 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_22.json b/sleep_assessment/cases/case14/case14_22.json deleted file mode 100644 index fede014f03745629c2ca170e977de41bd3cfbb04..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_22.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 40.7979407979408, - "tolerance": 2.0398970398970397 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 17.245817245817246, - "tolerance": 0.8622908622908624 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 39.12483912483913, - "tolerance": 1.9562419562419564 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 2.7027027027027026, - "tolerance": 0.13513513513513514 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 0.1287001287001287, - "tolerance": 0.0064350064350064354 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_23.json b/sleep_assessment/cases/case14/case14_23.json deleted file mode 100644 index 0005b4866171df5d0d6a0e77eaddbd53f9708b60..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_23.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 53.78323108384458, - "tolerance": 2.6891615541922294 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 5.828220858895706, - "tolerance": 0.2914110429447853 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 33.94683026584867, - "tolerance": 1.6973415132924334 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 6.339468302658487, - "tolerance": 0.3169734151329244 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 0.10224948875255624, - "tolerance": 0.005112474437627812 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_24.json b/sleep_assessment/cases/case14/case14_24.json deleted file mode 100644 index 7416c299504d52882fcc43db4946940515ef9a1f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_24.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 9.597156398104266, - "tolerance": 0.4798578199052133 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 19.075829383886255, - "tolerance": 0.9537914691943128 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 40.28436018957346, - "tolerance": 2.014218009478673 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 20.379146919431278, - "tolerance": 1.018957345971564 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 10.663507109004739, - "tolerance": 0.533175355450237 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case14/case14_25.json b/sleep_assessment/cases/case14/case14_25.json deleted file mode 100644 index fcfe34550818f2c19d5167dfe8d0d5d3656cb85e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case14/case14_25.json +++ /dev/null @@ -1,67 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the percentage distribution of the five sleep stages W, N1, N2, N3, and R. Report the percentage of the total analyzed sleep-stage sequence occupied by each stage. Please clearly report all five final percentage values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract five sleep-stage percentage values from the agent's report:\n1) W stage percentage\n2) N1 stage percentage\n3) N2 stage percentage\n4) N3 stage percentage\n5) R/REM stage percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"w_stage_ratio_percent\", \"n1_stage_ratio_percent\", \"n2_stage_ratio_percent\", \"n3_stage_ratio_percent\", and \"r_stage_ratio_percent\".\n5. All values must be FLOATS or null.\n6. Values are percentages; if the report says 25.5%, output 25.5.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"w_stage_ratio_percent\": , \"n1_stage_ratio_percent\": , \"n2_stage_ratio_percent\": , \"n3_stage_ratio_percent\": , \"r_stage_ratio_percent\": }", - "metrics": [ - { - "metric_id": "w_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "w_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 6.152241918665276, - "tolerance": 0.30761209593326383 - } - }, - { - "metric_id": "n1_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n1_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 34.82794577685089, - "tolerance": 1.7413972888425446 - } - }, - { - "metric_id": "n2_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n2_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 38.06047966631908, - "tolerance": 1.9030239833159541 - } - }, - { - "metric_id": "n3_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "n3_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 10.323253388946819, - "tolerance": 0.516162669447341 - } - }, - { - "metric_id": "r_stage_ratio_percent_accuracy", - "type": "numeric_check", - "target_key": "r_stage_ratio_percent", - "weight": 20, - "params": { - "gt_value": 10.636079249217936, - "tolerance": 0.5318039624608968 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_01.json b/sleep_assessment/cases/case15/case15_01.json deleted file mode 100644 index 757e543700336ee71be349b8b2e540963aaa7701..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_01.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_02.json b/sleep_assessment/cases/case15/case15_02.json deleted file mode 100644 index e4ed2485030491c3eb864f690b54626127c40eb3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_02.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_03.json b/sleep_assessment/cases/case15/case15_03.json deleted file mode 100644 index cd9fb5bb6b8d1826de6ba31a56d440a731468dd4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_03.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_04.json b/sleep_assessment/cases/case15/case15_04.json deleted file mode 100644 index d576a65508a5dcc347322b2bce1d7540f3bfb28c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_04.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_05.json b/sleep_assessment/cases/case15/case15_05.json deleted file mode 100644 index 56111bf68a3f71ddff92a6565cc4864adca4324a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_05.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_06.json b/sleep_assessment/cases/case15/case15_06.json deleted file mode 100644 index fee6ae815cd426ac68b5f6c17283ccc771bd6f1d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_06.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_07.json b/sleep_assessment/cases/case15/case15_07.json deleted file mode 100644 index 838f795e1cbf14ba41172fe9017de93029ef0f93..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_07.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_08.json b/sleep_assessment/cases/case15/case15_08.json deleted file mode 100644 index f6e91865961d845b86347e02f1de17e1983dfcd4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_08.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_09.json b/sleep_assessment/cases/case15/case15_09.json deleted file mode 100644 index 30084b9a1e0f303c91627b83c5b652ea35d72c14..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_09.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_10.json b/sleep_assessment/cases/case15/case15_10.json deleted file mode 100644 index 75a9b4903646b51e436969c11944042e678091e1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_10.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Abnormal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_11.json b/sleep_assessment/cases/case15/case15_11.json deleted file mode 100644 index c49cebd50fc0f71571f02326dde24a4260ac097b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_11.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_12.json b/sleep_assessment/cases/case15/case15_12.json deleted file mode 100644 index 6a119e10cd7f7f07c9f92c65b768fbc6bc67708f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_12.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_13.json b/sleep_assessment/cases/case15/case15_13.json deleted file mode 100644 index 3014d86c3ccc672d80d542554569f5f02728ddc3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_13.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Abnormal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_14.json b/sleep_assessment/cases/case15/case15_14.json deleted file mode 100644 index 2549cbcd5cb1d3d71524d7287fe728a4ff2529ec..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_14.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Abnormal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_15.json b/sleep_assessment/cases/case15/case15_15.json deleted file mode 100644 index c3184b3866fc6cbe73b9123bb491f535328c4c50..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_15.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_16.json b/sleep_assessment/cases/case15/case15_16.json deleted file mode 100644 index 32453c1567a139fbe3a4997dd36bef479e5a6ca8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_16.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_17.json b/sleep_assessment/cases/case15/case15_17.json deleted file mode 100644 index b3ac3c99c17f4242c40f1442f86fbee384f97ec7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_17.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_18.json b/sleep_assessment/cases/case15/case15_18.json deleted file mode 100644 index f838d83d9760c2ee89a3372065af2cb4f3e29698..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_18.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_19.json b/sleep_assessment/cases/case15/case15_19.json deleted file mode 100644 index db0f5c05bbd969897beac7a09fea00876467e51f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_19.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_20.json b/sleep_assessment/cases/case15/case15_20.json deleted file mode 100644 index f2073f22b434c512f7ccf545076be30b81258238..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_20.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_21.json b/sleep_assessment/cases/case15/case15_21.json deleted file mode 100644 index 706690a762ed54d65b78410ff316beede8fea806..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_21.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Abnormal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_22.json b/sleep_assessment/cases/case15/case15_22.json deleted file mode 100644 index e1eae7ca8a369be0b60bd5dbe7a63b263c04f02b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_22.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_23.json b/sleep_assessment/cases/case15/case15_23.json deleted file mode 100644 index 30a91275e689e2679f6c312ae53e1f80770819d5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_23.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Abnormal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_24.json b/sleep_assessment/cases/case15/case15_24.json deleted file mode 100644 index 936566688b9232c889a5f8fc550ac336ca82d0d7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_24.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case15/case15_25.json b/sleep_assessment/cases/case15/case15_25.json deleted file mode 100644 index 8f78130932f84270e0b3b3a51d5e85785e46c5e0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case15/case15_25.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to determine whether each of the following sleep-structure abnormalities is present: A. N3 absent, B. REM absent, C. excessive wake proportion, D. low deep sleep (N3 deficit), E. low REM proportion, F. N2 dominance, G. early REM (short REM latency), H. abnormal N3 distribution (not front-loaded), I. abnormal REM distribution (not back-loaded), J. high fragmentation, K. excessive awakenings, L. short continuous sleep. Please return the abnormality letters that are present." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nThere are EXACTLY 12 candidate abnormality options: A, B, C, D, E, F, G, H, I, J, K, and L. Read the agent's report and determine for each letter whether the report indicates that the abnormality is present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY these 12 keys: \"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\", \"J\", \"K\", \"L\".\n5. Each value must be EXACTLY either \"Normal\" or \"Abnormal\".\n6. If the report explicitly lists or clearly states that a given abnormality exists, output \"Abnormal\" for that letter.\n7. Otherwise output \"Normal\" for that letter.\n8. Do NOT perform medical reasoning beyond extracting what the report says.\n\n### OUTPUT TEMPLATE\n{\"A\": \"Normal|Abnormal\", \"B\": \"Normal|Abnormal\", \"C\": \"Normal|Abnormal\", \"D\": \"Normal|Abnormal\", \"E\": \"Normal|Abnormal\", \"F\": \"Normal|Abnormal\", \"G\": \"Normal|Abnormal\", \"H\": \"Normal|Abnormal\", \"I\": \"Normal|Abnormal\", \"J\": \"Normal|Abnormal\", \"K\": \"Normal|Abnormal\", \"L\": \"Normal|Abnormal\"}", - "metrics": [ - { - "metric_id": "A_abnormality_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "B_abnormality_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "C_abnormality_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "D_abnormality_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "E_abnormality_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "F_abnormality_accuracy", - "type": "categorical_check", - "target_key": "F", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "G_abnormality_accuracy", - "type": "categorical_check", - "target_key": "G", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "H_abnormality_accuracy", - "type": "categorical_check", - "target_key": "H", - "weight": 8.3333333333, - "params": { - "gt_value": "Abnormal" - } - }, - { - "metric_id": "I_abnormality_accuracy", - "type": "categorical_check", - "target_key": "I", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "J_abnormality_accuracy", - "type": "categorical_check", - "target_key": "J", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "K_abnormality_accuracy", - "type": "categorical_check", - "target_key": "K", - "weight": 8.3333333333, - "params": { - "gt_value": "Normal" - } - }, - { - "metric_id": "L_abnormality_accuracy", - "type": "categorical_check", - "target_key": "L", - "weight": 8.3333333337, - "params": { - "gt_value": "Normal" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case16/case16_01.json b/sleep_assessment/cases/case16/case16_01.json deleted file mode 100644 index cea6449a1f62fa96834db39cc882c8244e2af1c3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_01.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 218.5 to minute 221.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N1" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_02.json b/sleep_assessment/cases/case16/case16_02.json deleted file mode 100644 index e3633cc60589a5c753d5baa8ff16ac3e45731c4b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_02.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 464.0 to minute 467.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_03.json b/sleep_assessment/cases/case16/case16_03.json deleted file mode 100644 index 3b61f96a5b70caae7b161c9c3a1920d9ae4bb190..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_03.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 0.5 to minute 3.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "alpha" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_04.json b/sleep_assessment/cases/case16/case16_04.json deleted file mode 100644 index f5bb0851e4b4d7586e5f1d27e08be46d355e9f25..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_04.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 42.5 to minute 45.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_05.json b/sleep_assessment/cases/case16/case16_05.json deleted file mode 100644 index f8f14291d22a0ac8b74dbd6a5010e42a63d1a232..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_05.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 196.5 to minute 199.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_06.json b/sleep_assessment/cases/case16/case16_06.json deleted file mode 100644 index 4576cf243bcdee97f00ad324b03ce26973b0d81f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_06.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 120.5 to minute 123.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N1" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_07.json b/sleep_assessment/cases/case16/case16_07.json deleted file mode 100644 index 14fa27264706abaafb629bef4f705d4fba806e63..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_07.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 303.5 to minute 306.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_08.json b/sleep_assessment/cases/case16/case16_08.json deleted file mode 100644 index 8a5b6984f4c695ef7142d166c194f4dca0bab7f2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_08.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 234.0 to minute 237.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_09.json b/sleep_assessment/cases/case16/case16_09.json deleted file mode 100644 index 4a8a985552963d5c7e53a3065c4f008b6ee82717..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_09.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 416.5 to minute 419.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_10.json b/sleep_assessment/cases/case16/case16_10.json deleted file mode 100644 index 1092a48854c3dd8b8627d87cf297b27aed16bc6d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_10.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 398.5 to minute 401.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_11.json b/sleep_assessment/cases/case16/case16_11.json deleted file mode 100644 index f1a513e6d2e8a152bd094e40171b973590f4a31b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_11.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 149.0 to minute 152.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_12.json b/sleep_assessment/cases/case16/case16_12.json deleted file mode 100644 index fb09c848b4abb9a2770e6f13c8e1037445e6bf5e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_12.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 536.5 to minute 539.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "alpha" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_13.json b/sleep_assessment/cases/case16/case16_13.json deleted file mode 100644 index 1acd834fd7f75abd55717a769d5718444f90597c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_13.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 440.0 to minute 443.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N1" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_14.json b/sleep_assessment/cases/case16/case16_14.json deleted file mode 100644 index c0a3c8d42ce43ec2dd025b62dbc4e8c829ef5896..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_14.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 149.5 to minute 152.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "theta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_15.json b/sleep_assessment/cases/case16/case16_15.json deleted file mode 100644 index 597003093e033ce293c883b988cda2f2a15997a6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_15.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 118.5 to minute 121.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_16.json b/sleep_assessment/cases/case16/case16_16.json deleted file mode 100644 index 01f798110dade8e9e3745c01e5bcb8aa895ef444..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_16.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 343.5 to minute 346.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_17.json b/sleep_assessment/cases/case16/case16_17.json deleted file mode 100644 index ab8ebd90ca227bd7458b103aa53958ed850a2531..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_17.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 407.5 to minute 410.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "theta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_18.json b/sleep_assessment/cases/case16/case16_18.json deleted file mode 100644 index a66444ec3cfbc86b92d0d1ad3c1eb03d733e238a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_18.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 176.5 to minute 179.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_19.json b/sleep_assessment/cases/case16/case16_19.json deleted file mode 100644 index 63f27e20fc546d08c2315e59416d1c5a7ac3645b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_19.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 5.0 to minute 8.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N1" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_20.json b/sleep_assessment/cases/case16/case16_20.json deleted file mode 100644 index 0908358a0c5b773b0c7897caf14b28abef865766..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_20.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 167.0 to minute 170.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_21.json b/sleep_assessment/cases/case16/case16_21.json deleted file mode 100644 index eacfbdc50ecf11cfd78cd9cc5428fd6714b0cd0e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_21.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 25.0 to minute 28.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_22.json b/sleep_assessment/cases/case16/case16_22.json deleted file mode 100644 index c6fd6425b1474770aaad579fccd2a2e062496821..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_22.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 225.5 to minute 228.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "beta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N1" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_23.json b/sleep_assessment/cases/case16/case16_23.json deleted file mode 100644 index 55df033003be34a2855ddd13ab4f1308843836cf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_23.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 420.0 to minute 423.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "beta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_24.json b/sleep_assessment/cases/case16/case16_24.json deleted file mode 100644 index 1594cb380c70ac58c361c8fc915ffd628346f7a3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_24.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 121.0 to minute 124.0. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case16/case16_25.json b/sleep_assessment/cases/case16/case16_25.json deleted file mode 100644 index 970108ad41c061470014820c7a122ecad4adcd49..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case16/case16_25.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please use the provided sleep file to analyze the segment from minute 370.5 to minute 373.5. Determine the dominant frequency band of this segment and the sleep stage of this segment. Report the dominant band using one of: delta, theta, alpha, beta, gamma, None. Report the sleep stage using one of: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the dominant frequency band of the specified segment\n2) the sleep stage of the specified segment\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"dominant_band\" and \"sleep_stage\".\n5. \"dominant_band\" must be exactly one of: \"delta\", \"theta\", \"alpha\", \"beta\", \"gamma\", or null.\n6. \"sleep_stage\" must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_band\": <\"delta\"|\"theta\"|\"alpha\"|\"beta\"|\"gamma\"|null>, \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_band_accuracy", - "type": "categorical_check", - "target_key": "dominant_band", - "weight": 50, - "params": { - "gt_value": "delta" - } - }, - { - "metric_id": "sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 50, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_01.json b/sleep_assessment/cases/case17/case17_01.json deleted file mode 100644 index ad1c88ce13f962528875705ee5b6a0477a0e6e9c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_01.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 638 to epoch 647. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N3", - "N2", - "N3", - "N3", - "N3", - "W", - "N1", - "N1", - "N2", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_02.json b/sleep_assessment/cases/case17/case17_02.json deleted file mode 100644 index 641c9f30e7855cd08c8808334c131402ac31d4b0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_02.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 495 to epoch 504. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "R", - "W", - "W", - "N1", - "N2", - "N1", - "N2", - "N2", - "N1", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_03.json b/sleep_assessment/cases/case17/case17_03.json deleted file mode 100644 index c82f3228fb20ca1ccde8ba3fe35617184b385bcd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_03.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 849 to epoch 858. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N3", - "W", - "W", - "N1", - "N1", - "N1", - "N1", - "N2", - "W", - "N1" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_04.json b/sleep_assessment/cases/case17/case17_04.json deleted file mode 100644 index 9e53a19e50f8002a33742872e1593849de9dc6e9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_04.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 82 to epoch 91. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N3", - "W", - "N1", - "N2", - "N2", - "N2", - "N2", - "N2", - "N2", - "N1" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_05.json b/sleep_assessment/cases/case17/case17_05.json deleted file mode 100644 index 0f2270f2ddfa1670a2f8294ad5313f38cfab92db..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_05.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 599 to epoch 608. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N2", - "R", - "R", - "R", - "N1", - "N1", - "N1", - "N1", - "W", - "N1" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_06.json b/sleep_assessment/cases/case17/case17_06.json deleted file mode 100644 index a465b60a4ab86cf881d11ba30118a7089570fd01..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_06.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 647 to epoch 656. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N3", - "N2", - "N3", - "N2", - "N2", - "W", - "W", - "W", - "N1", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_07.json b/sleep_assessment/cases/case17/case17_07.json deleted file mode 100644 index 912ec14887323148aeee220ab07decee14fedbc5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_07.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 5 to epoch 14. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "W", - "N1", - "N1", - "N1", - "N2", - "N2", - "N2", - "N3", - "N2", - "N3" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_08.json b/sleep_assessment/cases/case17/case17_08.json deleted file mode 100644 index 85fc16a3c4dce2c7dce2e39a99b30d6e8bc82d2c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_08.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 81 to epoch 90. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N3", - "N3", - "N1", - "N2", - "N2", - "N3", - "N3", - "W", - "W", - "W" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_09.json b/sleep_assessment/cases/case17/case17_09.json deleted file mode 100644 index 8673051442b30a1a446cb638e3a2b02b3e0857b2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_09.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 110 to epoch 119. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "R", - "R", - "R", - "W", - "W", - "W", - "N1", - "N1", - "N2", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_10.json b/sleep_assessment/cases/case17/case17_10.json deleted file mode 100644 index 6f75fe66b3edabdb97f53d6f7f09e84ddf347db0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_10.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 926 to epoch 935. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "R", - "N1", - "N2", - "R", - "R", - "W", - "N1", - "N1", - "N2", - "N1" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_11.json b/sleep_assessment/cases/case17/case17_11.json deleted file mode 100644 index 7686c84e77aa427037880d13da89aaa895f91efc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_11.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 452 to epoch 461. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N2", - "N3", - "N2", - "N2", - "N2", - "N2", - "W", - "N1", - "N2", - "R" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_12.json b/sleep_assessment/cases/case17/case17_12.json deleted file mode 100644 index 48231f1950b1357dde976f858e46688fa2e826cb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_12.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 29 to epoch 38. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N2", - "N3", - "N2", - "N2", - "N3", - "N2", - "N3", - "N2", - "W", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_13.json b/sleep_assessment/cases/case17/case17_13.json deleted file mode 100644 index 7ef07c8d2f3524b1d27d4613c8c7a6ad1c498e8b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_13.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 532 to epoch 541. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "R", - "R", - "N2", - "W", - "N2", - "W", - "W", - "N1", - "N1", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_14.json b/sleep_assessment/cases/case17/case17_14.json deleted file mode 100644 index 49b105b151dea962db8736fae918beb2e8e1a61d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_14.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 120 to epoch 129. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N3", - "N3", - "N2", - "N3", - "W", - "N1", - "N2", - "N3", - "N2", - "N3" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_15.json b/sleep_assessment/cases/case17/case17_15.json deleted file mode 100644 index 9d6e124016b08763f037642ddfd13b5f6f3095be..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_15.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 157 to epoch 166. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N3", - "N2", - "N3", - "N3", - "N3", - "W", - "W", - "N1", - "W", - "W" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_16.json b/sleep_assessment/cases/case17/case17_16.json deleted file mode 100644 index 693daf2fc58ea6f7cc8d4af35f02b1e15882469a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_16.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 5 to epoch 14. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "W", - "W", - "N1", - "N1", - "N1", - "N1", - "N2", - "N2", - "N3", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_17.json b/sleep_assessment/cases/case17/case17_17.json deleted file mode 100644 index d18aefe119009187a92ee502393c3d4266470e58..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_17.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 192 to epoch 201. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N2", - "N3", - "N2", - "N3", - "W", - "W", - "N1", - "W", - "N1", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_18.json b/sleep_assessment/cases/case17/case17_18.json deleted file mode 100644 index 2765d1786f1dc6a890a0504ab76e29671eeb0be4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_18.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 745 to epoch 754. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "R", - "R", - "W", - "N1", - "N1", - "N2", - "N2", - "N2", - "N2", - "W" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_19.json b/sleep_assessment/cases/case17/case17_19.json deleted file mode 100644 index 1ff0ce50b2434c787efe78d446b514565bbf35fb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_19.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 489 to epoch 498. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N1", - "N1", - "W", - "N1", - "N2", - "N2", - "N2", - "N2", - "N2", - "R" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_20.json b/sleep_assessment/cases/case17/case17_20.json deleted file mode 100644 index 74a54e5f2e71f988faf609b704712bfee894b2c6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_20.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 711 to epoch 720. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N2", - "N3", - "N3", - "N2", - "N2", - "N3", - "N2", - "N3", - "W", - "N1" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_21.json b/sleep_assessment/cases/case17/case17_21.json deleted file mode 100644 index 50bf833683bd4e5109022db44f4a8c0253989df8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_21.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 454 to epoch 463. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "R", - "R", - "R", - "W", - "N1", - "N2", - "N2", - "N2", - "N1", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_22.json b/sleep_assessment/cases/case17/case17_22.json deleted file mode 100644 index 3e3c1784dc842497259afedf72a752b3ed552c62..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_22.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 72 to epoch 81. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N2", - "N1", - "N1", - "N2", - "N2", - "W", - "N1", - "N2", - "N1", - "N2" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_23.json b/sleep_assessment/cases/case17/case17_23.json deleted file mode 100644 index 3f9f03aa2c6d23aed8cc6479c36d88426fe96141..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_23.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 242 to epoch 251. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N2", - "N2", - "N3", - "N2", - "N3", - "N2", - "W", - "N1", - "W", - "W" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_24.json b/sleep_assessment/cases/case17/case17_24.json deleted file mode 100644 index 9d49282f0d387aee97ead5cbd3b600ae28487df2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_24.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 728 to epoch 737. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "R", - "N1", - "N1", - "R", - "R", - "W", - "N1", - "N1", - "N2", - "N1" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case17/case17_25.json b/sleep_assessment/cases/case17/case17_25.json deleted file mode 100644 index 261d7d1e063dcb959b98be8643e10542a44fb8e0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case17/case17_25.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please use the provided sleep file to determine the sleep stages of the 10 consecutive epochs from epoch 78 to epoch 87. Return the corresponding 10-label sleep-stage sequence using only: W, N1, N2, N3, R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the 10-label sleep-stage sequence from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON must contain EXACTLY one key named \"sleep_stage_sequence\".\n5. The value of \"sleep_stage_sequence\" must be a JSON array with EXACTLY 10 elements.\n6. Every element must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\".\n7. Do NOT output Chinese labels, integers, booleans, or null placeholders inside the list.\n8. If the report is unclear, still return the JSON key, but use an empty array only if no sequence can be extracted.\n\n### OUTPUT TEMPLATE\n{\"sleep_stage_sequence\": [\"W\", \"N1\", \"N2\", \"N3\", \"R\", \"W\", \"N1\", \"N2\", \"N3\", \"R\"]}", - "metrics": [ - { - "metric_id": "sleep_stage_sequence_accuracy", - "type": "sequence_match_check", - "target_key": "sleep_stage_sequence", - "weight": 100, - "params": { - "gt_value": [ - "N3", - "N3", - "N3", - "N3", - "N2", - "N2", - "N1", - "N2", - "N1", - "N1" - ], - "match_mode": "per_hit_fraction_order" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_01.json b/sleep_assessment/cases/case18/case18_01.json deleted file mode 100644 index f9b40a390df96fcf1f54f9995277716c72ea7dcc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_01.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename ISRUC_01_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_01_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_02.json b/sleep_assessment/cases/case18/case18_02.json deleted file mode 100644 index 22fbcc09a740066810f63a347969c8cd36a053dd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_02.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename ISRUC_02_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_02_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_03.json b/sleep_assessment/cases/case18/case18_03.json deleted file mode 100644 index 8fe9f64c6b9beff9f0dcf2d0a3dcee7c409918f6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_03.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename ISRUC_03_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_03_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_04.json b/sleep_assessment/cases/case18/case18_04.json deleted file mode 100644 index ac42aed3fb28a21d3cfe7e4d81f05c987249f1e4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_04.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename ISRUC_04_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_04_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_05.json b/sleep_assessment/cases/case18/case18_05.json deleted file mode 100644 index a2036b104997f4a22f9fb6c55ed9766e61f5f129..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_05.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename ISRUC_05_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_05_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_06.json b/sleep_assessment/cases/case18/case18_06.json deleted file mode 100644 index c104e43b0d5db1a16f7c4b7331619ba891a23405..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_06.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename HMC_01_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_01_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_07.json b/sleep_assessment/cases/case18/case18_07.json deleted file mode 100644 index 1fb904e59ea1b0510a3f00399334a94aec0e9b4d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_07.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename HMC_02_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_02_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_08.json b/sleep_assessment/cases/case18/case18_08.json deleted file mode 100644 index 7475bf2ba7a7068c6b988649d25592e13fa8ad40..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_08.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename HMC_03_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_03_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_09.json b/sleep_assessment/cases/case18/case18_09.json deleted file mode 100644 index 6580c33fa06bb5a1c923b6f5e490247aa8034838..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_09.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename HMC_04_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_04_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_10.json b/sleep_assessment/cases/case18/case18_10.json deleted file mode 100644 index cb0b42a9a2cd12af0476d7137b1ce08a9ab01180..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_10.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename HMC_05_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_05_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_11.json b/sleep_assessment/cases/case18/case18_11.json deleted file mode 100644 index 337b70a30abfed971fd8b7c8fcdfed4a18342175..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_11.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename SHHS1_01_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_01_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_12.json b/sleep_assessment/cases/case18/case18_12.json deleted file mode 100644 index bca5b4d674a04611f3284fb0b0be3d18d1cebbb0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_12.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename SHHS1_02_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_02_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_13.json b/sleep_assessment/cases/case18/case18_13.json deleted file mode 100644 index 66bd5ba4d5bcd6e75b0e6e86e315e87293548ba3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_13.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename SHHS1_03_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_03_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_14.json b/sleep_assessment/cases/case18/case18_14.json deleted file mode 100644 index 9b38d2410519ac124936636dad00cc1cdc56c2a9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_14.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename SHHS1_04_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_04_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_15.json b/sleep_assessment/cases/case18/case18_15.json deleted file mode 100644 index c08cb8de982c226b95223600d199b6bed1a692b1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_15.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename SHHS1_05_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_05_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_16.json b/sleep_assessment/cases/case18/case18_16.json deleted file mode 100644 index 12a912b751db86a321e6ce3c610a608711470ae1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_16.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename MASSSS3_01_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_01_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_17.json b/sleep_assessment/cases/case18/case18_17.json deleted file mode 100644 index 5560fc2810de7d624d5795d82892a65e5c3fa240..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_17.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename MASSSS3_02_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_02_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_18.json b/sleep_assessment/cases/case18/case18_18.json deleted file mode 100644 index d701d9125a424694d3063ca2bafa5fac5a040500..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_18.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename MASSSS3_03_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_03_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_19.json b/sleep_assessment/cases/case18/case18_19.json deleted file mode 100644 index 1fdd4093c92f0e4032c8e38b6df2026b5ff24216..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_19.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename MASSSS3_04_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_04_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_20.json b/sleep_assessment/cases/case18/case18_20.json deleted file mode 100644 index fe1a936799235641cdf957f0886402fb4af44eec..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_20.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename MASSSS3_05_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_05_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_21.json b/sleep_assessment/cases/case18/case18_21.json deleted file mode 100644 index 1aabadcb5632ef4e547b5751a38c355e614c8208..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_21.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename Physionet2018_01_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_01_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_22.json b/sleep_assessment/cases/case18/case18_22.json deleted file mode 100644 index 975dc59e41c57206358212fed5bf332098c56bad..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_22.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename Physionet2018_02_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_02_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_23.json b/sleep_assessment/cases/case18/case18_23.json deleted file mode 100644 index d262e316669c65fa9f58f3662b75355931050f87..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_23.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename Physionet2018_03_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_03_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_24.json b/sleep_assessment/cases/case18/case18_24.json deleted file mode 100644 index 7409600cdbdd8d3f87a337ef744ffc730cd8c14b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_24.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename Physionet2018_04_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_04_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case18/case18_25.json b/sleep_assessment/cases/case18/case18_25.json deleted file mode 100644 index 30b7548005859dfa5d5268dce828c7bb92a1c4fe..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case18/case18_25.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please use the provided sleep file to generate a sleep hypnogram, and save the image to the file_check directory with the filename Physionet2018_05_case18_sleep_hypnogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep hypnogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_hypnogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_hypnogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_05_case18_sleep_hypnogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep hypnogram plotting task. Check the image only. The task requirement is to generate a sleep hypnogram, also known as a sleep-stage-over-time plot, from a raw sleep recording. Return pass only if the image is clearly a valid sleep hypnogram-style figure. Specifically verify: (1) the image shows sleep stages over time, epochs, or an overnight recording timeline; (2) the y-axis or visible labels represent discrete sleep stages such as W/Wake, N1, N2, N3, and REM/R; (3) the plotted content is a staircase line, stage trajectory, or equivalent discrete sleep-stage time series rather than continuous raw EEG amplitude; (4) the figure is not a PSD plot, spectrogram, heatmap, correlation matrix, bar chart, topomap, text report screenshot, or unrelated medical image; (5) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge whether the sleep-stage sequence is accurate, whether it matches ground-truth labels, or whether the clinical interpretation is correct. Do not require a specific color scheme or exact stage proportions. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case19/case19_01.json b/sleep_assessment/cases/case19/case19_01.json deleted file mode 100644 index 3073c63975ff9978c6685a15ca8b1723a1aca573..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_01.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename ISRUC_01_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_01_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 880, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 2.8576221746994855e-06, - "weight": 50, - "tolerance": 2.857622174699486e-07 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_02.json b/sleep_assessment/cases/case19/case19_02.json deleted file mode 100644 index 9c4a50fcae1a5156a6812f9dbbe14a6eea7b82f0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_02.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename ISRUC_02_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_02_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 964, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 7.150938501825713e-07, - "weight": 50, - "tolerance": 7.150938501825712e-08 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_03.json b/sleep_assessment/cases/case19/case19_03.json deleted file mode 100644 index 0febd9fe81d40f64297bf8e3ddb1ec055c17f252..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_03.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename ISRUC_03_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_03_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 943, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 9.646151925984784e-07, - "weight": 50, - "tolerance": 9.646151925984785e-08 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_04.json b/sleep_assessment/cases/case19/case19_04.json deleted file mode 100644 index 86474cb262104f2166c3b91d634105bf187365ad..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_04.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename ISRUC_04_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_04_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 963, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.6586367305902015e-06, - "weight": 50, - "tolerance": 1.6586367305902016e-07 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_05.json b/sleep_assessment/cases/case19/case19_05.json deleted file mode 100644 index f1306c5efbdfab2cb7ad5839f9612f5679e47417..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_05.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename ISRUC_05_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_05_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 875, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 9.007648216435279e-07, - "weight": 50, - "tolerance": 9.007648216435279e-08 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_06.json b/sleep_assessment/cases/case19/case19_06.json deleted file mode 100644 index 2e45143eeeabe4918e031c0f3925a80a61291c7c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_06.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename HMC_01_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "HMC_01_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 855, - 4, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.3020269313234744e-05, - "weight": 50, - "tolerance": 1.3020269313234744e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_07.json b/sleep_assessment/cases/case19/case19_07.json deleted file mode 100644 index 9464bd30f0c8d5e4e789a5e081817efef8f176c6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_07.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename HMC_02_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "HMC_02_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 856, - 4, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.2270847782234478e-05, - "weight": 50, - "tolerance": 1.2270847782234478e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_08.json b/sleep_assessment/cases/case19/case19_08.json deleted file mode 100644 index f5085bace2ce3c797adae4932c01873ab36d426a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_08.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename HMC_03_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "HMC_03_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 954, - 4, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.549550757606623e-05, - "weight": 50, - "tolerance": 1.5495507576066231e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_09.json b/sleep_assessment/cases/case19/case19_09.json deleted file mode 100644 index b20a1d2e6d0f071328c543172d2a5bc10e7dd5c3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_09.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename HMC_04_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "HMC_04_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 1016, - 4, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 6.450915364145694e-06, - "weight": 50, - "tolerance": 6.450915364145695e-07 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_10.json b/sleep_assessment/cases/case19/case19_10.json deleted file mode 100644 index 2b1de41a28a940c5edd425d6e9e446f450e4a18a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_10.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename HMC_05_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "HMC_05_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 959, - 4, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.56099408047631e-05, - "weight": 50, - "tolerance": 1.56099408047631e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_11.json b/sleep_assessment/cases/case19/case19_11.json deleted file mode 100644 index a09651a1a89ee3ef831627877eb6efad5455298d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_11.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename SHHS1_01_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_01_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 1084, - 2, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 2.3383364617041573e-05, - "weight": 50, - "tolerance": 2.3383364617041573e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_12.json b/sleep_assessment/cases/case19/case19_12.json deleted file mode 100644 index 9d73c882906611d72de4821fef4aba9b079077e4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_12.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename SHHS1_02_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_02_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 1079, - 2, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 5.887706030978153e-05, - "weight": 50, - "tolerance": 5.887706030978153e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_13.json b/sleep_assessment/cases/case19/case19_13.json deleted file mode 100644 index cbbba891099a10cefee033b738ba6843c1435701..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_13.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename SHHS1_03_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_03_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 1049, - 2, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.2323215794421976e-05, - "weight": 50, - "tolerance": 1.2323215794421976e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_14.json b/sleep_assessment/cases/case19/case19_14.json deleted file mode 100644 index f9e65951f869b5e3d68b3dbf1adb5d6f11ac6b61..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_14.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename SHHS1_04_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_04_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 875, - 2, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.038779187795578e-05, - "weight": 50, - "tolerance": 1.0387791877955781e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_15.json b/sleep_assessment/cases/case19/case19_15.json deleted file mode 100644 index f5a0c73a9ebcc66fffcc849be57cfc49503113f1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_15.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename SHHS1_05_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_05_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 1084, - 2, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 9.001139407932791e-06, - "weight": 50, - "tolerance": 9.001139407932792e-07 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_16.json b/sleep_assessment/cases/case19/case19_16.json deleted file mode 100644 index 72ed550dfdb48fe216ab9ad41f35922cf959e125..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_16.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename MASSSS3_01_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_01_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 1008, - 20, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 8.169859077706497e-06, - "weight": 50, - "tolerance": 8.169859077706497e-07 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_17.json b/sleep_assessment/cases/case19/case19_17.json deleted file mode 100644 index 38da174c2c3e79d93638f615d133cce490665a51..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_17.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename MASSSS3_02_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_02_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 1072, - 20, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 9.701064503925927e-06, - "weight": 50, - "tolerance": 9.701064503925927e-07 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_18.json b/sleep_assessment/cases/case19/case19_18.json deleted file mode 100644 index 084792471bfe01d1b882d0215c5dc54ce3034a09..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_18.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename MASSSS3_03_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_03_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 955, - 20, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.031449526907551e-05, - "weight": 50, - "tolerance": 1.031449526907551e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_19.json b/sleep_assessment/cases/case19/case19_19.json deleted file mode 100644 index 8227e12a048b37e7b8b67dbfae8fa9ec6810be08..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_19.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename MASSSS3_04_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_04_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 912, - 20, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 7.117451984621354e-06, - "weight": 50, - "tolerance": 7.117451984621354e-07 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_20.json b/sleep_assessment/cases/case19/case19_20.json deleted file mode 100644 index c1e36443aad4bbd9d44a6467671c03e1596fabf3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_20.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename MASSSS3_05_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_05_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 1069, - 20, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.102727722873399e-05, - "weight": 50, - "tolerance": 1.1027277228733992e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_21.json b/sleep_assessment/cases/case19/case19_21.json deleted file mode 100644 index 22198a1c94b80e65042989d1a43e4d2c7bdd7fcc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_21.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename Physionet2018_01_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_01_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 845, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 6.909229043019085e-05, - "weight": 50, - "tolerance": 6.909229043019086e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_22.json b/sleep_assessment/cases/case19/case19_22.json deleted file mode 100644 index 54b54b135ed980e652cf4cbf0548cba038427b1d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_22.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename Physionet2018_02_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_02_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 777, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.2305463073145908e-05, - "weight": 50, - "tolerance": 1.2305463073145909e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_23.json b/sleep_assessment/cases/case19/case19_23.json deleted file mode 100644 index 385e958d1f0c6bb55e33bc0de4798e06e91f7325..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_23.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename Physionet2018_03_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_03_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 978, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 2.5766899236262896e-05, - "weight": 50, - "tolerance": 2.5766899236262896e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_24.json b/sleep_assessment/cases/case19/case19_24.json deleted file mode 100644 index 434c1fd54088a4253b8935337709bcb0aadfe2f4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_24.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename Physionet2018_04_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_04_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 844, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.1769222877692592e-05, - "weight": 50, - "tolerance": 1.1769222877692592e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case19/case19_25.json b/sleep_assessment/cases/case19/case19_25.json deleted file mode 100644 index 6730c0f7c76970674e69f636cf2da073fbf0680d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case19/case19_25.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please use the provided sleep file to complete preprocessing for sleep staging. First select EEG channels only. Then apply a 60Hz notch filter because this case treats all Sleep subset data sources as non-China datasets, apply a 0.5-40Hz bandpass filter(apply IIR filtering), set an average reference, and resample to 100Hz. Next split the processed signal into non-overlapping 30-second epochs and save the data as a .npy array with shape (epoch_num, channel_num, 3000). Discard any final segment shorter than 30 seconds. Save the file to the file_check directory with the filename Physionet2018_05_case19_processed_data.npy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved preprocessed NPY file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved NPY file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_processed_data.npy\"}", - "metrics": [ - { - "metric_id": "processed_sleep_epoch_npy_status", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "eeg_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_05_case19_processed_data.npy" - }, - "npy_shape": { - "value": [ - 959, - 6, - 3000 - ], - "weight": 50 - }, - "signal_rms": { - "value": 1.5250553499268167e-05, - "weight": 50, - "tolerance": 1.5250553499268168e-06 - } - } - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case2/case2_01.json b/sleep_assessment/cases/case2/case2_01.json deleted file mode 100644 index 089b69cb9e979a552d32606fc1f13a198b86c6f5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_01.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 19, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "F3-A2", - "C3-A2", - "O1-A2", - "F4-A1", - "C4-A1", - "O2-A1" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_02.json b/sleep_assessment/cases/case2/case2_02.json deleted file mode 100644 index 50e05304ebf2e686cdaa1835c2a829ce2fbd1172..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_02.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 19, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "F3-A2", - "C3-A2", - "O1-A2", - "F4-A1", - "C4-A1", - "O2-A1" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_03.json b/sleep_assessment/cases/case2/case2_03.json deleted file mode 100644 index e39db6d9606a333646c2ff980b544064eac8ae99..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_03.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 19, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "F3-A2", - "C3-A2", - "O1-A2", - "F4-A1", - "C4-A1", - "O2-A1" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_04.json b/sleep_assessment/cases/case2/case2_04.json deleted file mode 100644 index 405f399610e52fe8738cbd15c52cb9767b0fa5d6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_04.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 8, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG F4-M1", - "EEG C4-M1", - "EEG O2-M1", - "EEG C3-M2" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_05.json b/sleep_assessment/cases/case2/case2_05.json deleted file mode 100644 index 107feb18f89abc8743790b233d0d8f8b4e9ab2c4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_05.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 8, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG F4-M1", - "EEG C4-M1", - "EEG O2-M1", - "EEG C3-M2" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_06.json b/sleep_assessment/cases/case2/case2_06.json deleted file mode 100644 index b28cf34a76adaeb467ca06e8e686b3d27e862986..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_06.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 8, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG F4-M1", - "EEG C4-M1", - "EEG O2-M1", - "EEG C3-M2" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_07.json b/sleep_assessment/cases/case2/case2_07.json deleted file mode 100644 index 48694927a80fe89b227a4e3afc3c3ec276ea3a98..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_07.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 14, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG(sec)", - "EEG" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_08.json b/sleep_assessment/cases/case2/case2_08.json deleted file mode 100644 index 9c728acbdc4a842016487b97444bce028561ca80..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_08.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 14, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG(sec)", - "EEG" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_09.json b/sleep_assessment/cases/case2/case2_09.json deleted file mode 100644 index 8d70cc6b07a509ca4d7264d8fdd57ec2021511db..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_09.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 14, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG(sec)", - "EEG" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_10.json b/sleep_assessment/cases/case2/case2_10.json deleted file mode 100644 index 590819106e05397d56f17c237399f9faaa34a027..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_10.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 27, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG Fp1-LER", - "EEG Fp2-LER", - "EEG F7-LER", - "EEG F8-LER", - "EEG F3-LER", - "EEG F4-LER", - "EEG T3-LER", - "EEG T4-LER", - "EEG C3-LER", - "EEG C4-LER", - "EEG T5-LER", - "EEG T6-LER", - "EEG P3-LER", - "EEG P4-LER", - "EEG O1-LER", - "EEG O2-LER", - "EEG Fz-LER", - "EEG Cz-LER", - "EEG Pz-LER", - "EEG Oz-LER" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_11.json b/sleep_assessment/cases/case2/case2_11.json deleted file mode 100644 index 9231bbe0cc5344b0afd39f401db752ddf1cd7077..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_11.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 27, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG Fp1-LER", - "EEG Fp2-LER", - "EEG F7-LER", - "EEG F8-LER", - "EEG F3-LER", - "EEG F4-LER", - "EEG T3-LER", - "EEG T4-LER", - "EEG C3-LER", - "EEG C4-LER", - "EEG T5-LER", - "EEG T6-LER", - "EEG P3-LER", - "EEG P4-LER", - "EEG O1-LER", - "EEG O2-LER", - "EEG Fz-LER", - "EEG Cz-LER", - "EEG Pz-LER", - "EEG Oz-LER" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_12.json b/sleep_assessment/cases/case2/case2_12.json deleted file mode 100644 index b34c2c889557f9c91565f7b3c365a38d51796dc1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_12.json +++ /dev/null @@ -1,58 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 27, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "EEG Fp1-LER", - "EEG Fp2-LER", - "EEG F7-LER", - "EEG F8-LER", - "EEG F3-LER", - "EEG F4-LER", - "EEG T3-LER", - "EEG T4-LER", - "EEG C3-LER", - "EEG C4-LER", - "EEG T5-LER", - "EEG T6-LER", - "EEG P3-LER", - "EEG P4-LER", - "EEG O1-LER", - "EEG O2-LER", - "EEG Fz-LER", - "EEG Cz-LER", - "EEG Pz-LER", - "EEG Oz-LER" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_13.json b/sleep_assessment/cases/case2/case2_13.json deleted file mode 100644 index 0aee35f9954abcc0f68fb49182dd1b9a47875d5d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_13.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 13, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "F3-M2", - "F4-M1", - "C3-M2", - "C4-M1", - "O1-M2", - "O2-M1" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_14.json b/sleep_assessment/cases/case2/case2_14.json deleted file mode 100644 index cf1009b5f78313510f2226924251ea81e9cda991..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_14.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 13, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "F3-M2", - "F4-M1", - "C3-M2", - "C4-M1", - "O1-M2", - "O2-M1" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case2/case2_15.json b/sleep_assessment/cases/case2/case2_15.json deleted file mode 100644 index 3c1c503132eb2cff1271dab40074e773c4f28f3a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case2/case2_15.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please inspect the provided raw sleep EEG/PSG file. Report the total number of channels in the file, and list all EEG channels that can be used for sleep analysis. Use the original channel names exactly as they appear in the raw file. Do not include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or other auxiliary channels in the EEG channel list." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG/PSG reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) the total number of channels in the raw file\n2) the list of all EEG channels using original raw-file channel names\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text or explanations.\n4. The keys must be EXACTLY \"channel_count\" and \"eeg_channels\".\n5. \"channel_count\" must be an INTEGER or null.\n6. \"eeg_channels\" must be a JSON array of strings or null.\n7. Preserve original EEG channel names exactly as written in the raw file.\n8. Do NOT normalize references, casing, spacing, parentheses, hyphens, or symbols.\n9. Do NOT include EOG, EMG, ECG, respiratory, SpO2/SaO2, light, position, or auxiliary channels in \"eeg_channels\".\n10. For SHHS1 reports, only \"EEG(sec)\" and \"EEG\" count as EEG channels; \"THOR RES\", \"POSITION\", and \"OX stat\" are not EEG channels.\n11. Remove duplicate EEG channel names if the report repeats them.\n12. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"channel_count\": , \"eeg_channels\": }", - "metrics": [ - { - "metric_id": "channel_count_accuracy", - "type": "numeric_check", - "target_key": "channel_count", - "weight": 30, - "params": { - "gt_value": 13, - "tolerance": 0.1 - } - }, - { - "metric_id": "eeg_channel_set_match", - "type": "set_match_check", - "target_key": "eeg_channels", - "weight": 70, - "params": { - "gt_value": [ - "F3-M2", - "F4-M1", - "C3-M2", - "C4-M1", - "O1-M2", - "O2-M1" - ], - "match_mode": "exact" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_01.json b/sleep_assessment/cases/case20/case20_01.json deleted file mode 100644 index 5cc23f16da473b0b6ff036466010a1b57ec4d0a1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_01.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename ISRUC_01_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_01_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_02.json b/sleep_assessment/cases/case20/case20_02.json deleted file mode 100644 index d533728416745eca112aaa87e6d00f6aeea011a5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_02.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename ISRUC_02_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_02_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_03.json b/sleep_assessment/cases/case20/case20_03.json deleted file mode 100644 index 7cf5b6e92b90aaf9119d691d5f67797a05378038..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_03.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename ISRUC_03_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_03_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_04.json b/sleep_assessment/cases/case20/case20_04.json deleted file mode 100644 index 2f0e644e18d39600a69537a001ec51fdb66aefbc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_04.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename ISRUC_04_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_04_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_05.json b/sleep_assessment/cases/case20/case20_05.json deleted file mode 100644 index 1dc215a5694f3a6a2f15756f1f94114fc551c7bd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_05.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename ISRUC_05_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "ISRUC_05_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_06.json b/sleep_assessment/cases/case20/case20_06.json deleted file mode 100644 index 3393fda48adbaa0b725a19582fbdbfe87fb2bdf0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_06.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename HMC_01_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_01_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_07.json b/sleep_assessment/cases/case20/case20_07.json deleted file mode 100644 index b850b615467cbf73bb95303a281f4ad6c4fd995c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_07.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename HMC_02_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_02_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_08.json b/sleep_assessment/cases/case20/case20_08.json deleted file mode 100644 index c5b11e309cbb9d50bc34c989335d82bfccd38b96..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_08.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename HMC_03_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_03_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_09.json b/sleep_assessment/cases/case20/case20_09.json deleted file mode 100644 index 60e1b8dc352bb05181e5b97e41df9faf5ae324a1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_09.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename HMC_04_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_04_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_10.json b/sleep_assessment/cases/case20/case20_10.json deleted file mode 100644 index 0ef700662a42f953f47bfc7287f762fc483233d1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_10.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename HMC_05_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "HMC_05_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_11.json b/sleep_assessment/cases/case20/case20_11.json deleted file mode 100644 index eac421af2c683ac75354729558ce40257dbea5b2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_11.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename SHHS1_01_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_01_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_12.json b/sleep_assessment/cases/case20/case20_12.json deleted file mode 100644 index 961c3fd2b73fbbf751414e393996aab5842dce48..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_12.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename SHHS1_02_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_02_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_13.json b/sleep_assessment/cases/case20/case20_13.json deleted file mode 100644 index 9738da21b22fc63760e4ebdd733ea2ea72e5dea2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_13.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename SHHS1_03_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_03_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_14.json b/sleep_assessment/cases/case20/case20_14.json deleted file mode 100644 index a552deb5a9eecf985d2cfe613bb3977123aa075e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_14.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename SHHS1_04_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_04_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_15.json b/sleep_assessment/cases/case20/case20_15.json deleted file mode 100644 index 07751c34da9759bd9c96ea9a7032e44c647aa789..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_15.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename SHHS1_05_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "SHHS1_05_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_16.json b/sleep_assessment/cases/case20/case20_16.json deleted file mode 100644 index 4652e0cacf8ca5bffd54adf508a54c0be6ba10a0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_16.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename MASSSS3_01_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_01_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_17.json b/sleep_assessment/cases/case20/case20_17.json deleted file mode 100644 index 1816c95027a8f06b2f83b500fd295cd8ad3e9b3f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_17.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename MASSSS3_02_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_02_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_18.json b/sleep_assessment/cases/case20/case20_18.json deleted file mode 100644 index 64252cdacf8c5f53b13850eba5c0381bb430d248..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_18.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename MASSSS3_03_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_03_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_19.json b/sleep_assessment/cases/case20/case20_19.json deleted file mode 100644 index 85d220922df40ed48cba120cea137e6c4576f0b3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_19.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename MASSSS3_04_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_04_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_20.json b/sleep_assessment/cases/case20/case20_20.json deleted file mode 100644 index 5518c49a75375ac206ac50431cb462c41f47078c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_20.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename MASSSS3_05_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "MASSSS3_05_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_21.json b/sleep_assessment/cases/case20/case20_21.json deleted file mode 100644 index 1bd5ea8cfeffef2c684cf8ca6c45fcc85703b8f0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_21.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename Physionet2018_01_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_01_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_22.json b/sleep_assessment/cases/case20/case20_22.json deleted file mode 100644 index b13e322cc65dc5849ae4f73d514d7dd19f7d2739..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_22.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename Physionet2018_02_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_02_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_23.json b/sleep_assessment/cases/case20/case20_23.json deleted file mode 100644 index c341099fa14f0b123f247dcb9b613e5c4fb7d3d1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_23.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename Physionet2018_03_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_03_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_24.json b/sleep_assessment/cases/case20/case20_24.json deleted file mode 100644 index 01fde99c302cb5fb5a0a3d6ad915cf5d00586b64..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_24.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename Physionet2018_04_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_04_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case20/case20_25.json b/sleep_assessment/cases/case20/case20_25.json deleted file mode 100644 index ca12bee2a5ba187fead8fba7c131245e8d9eebb6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case20/case20_25.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please use the provided sleep file, select EEG channels, draw a sleep EEG spectrogram, and save the image to the file_check directory with the filename Physionet2018_05_case20_sleep_spectrogram.png." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the saved sleep EEG spectrogram image file path from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The JSON key must be EXACTLY \"file_path\".\n5. The value must be a string file path or null.\n6. If the report does not clearly provide a saved image file path, return null.\n\n### OUTPUT TEMPLATE\n{\"file_path\": \"file_check/example_sleep_spectrogram.png\"}", - "metrics": [ - { - "metric_id": "sleep_eeg_spectrogram_png", - "type": "file_status_check", - "target_key": "file_path", - "weight": 100, - "params": { - "match_mode": "image_file_match", - "validator_config": { - "file_name": { - "value": "Physionet2018_05_case20_sleep_spectrogram.png" - }, - "vlm_prompt": { - "value": "You are judging whether a saved image correctly satisfies a sleep EEG spectrogram plotting task. Check the image only. The task requirement is to select EEG channels from a sleep recording and generate a sleep EEG spectrogram, also known as a time-frequency representation. Return pass only if the image is clearly a valid EEG sleep spectrogram-style figure. Specifically verify: (1) the image shows time, epochs, recording progress, or an overnight timeline on one axis; (2) the image shows frequency or frequency bands, typically labeled in Hz or as frequency, on another axis; (3) the plotted content is a color-intensity spectrogram, heatmap-like time-frequency map, STFT, wavelet, or PSD-over-time representation rather than a raw signal trace; (4) the figure can show a single EEG channel, multiple EEG-channel subplots, or an average/representative EEG-channel spectrogram; (5) the figure is not a sleep hypnogram, raw EEG waveform, single PSD curve, topomap, correlation matrix, bar chart, text report screenshot, or unrelated medical image; (6) the image is not blank, corrupted, pure black, pure white, or only an empty coordinate frame. Do not judge exact spectral power values, frequency resolution, time-window length, color map, units, normalization method, agreement with ground-truth labels, sleep-stage accuracy, or clinical interpretation. Do not require specific EEG channel names to be visible. Output only a valid JSON object. The JSON must contain exactly one key named status. The value of status must be either true or false. Do not output Markdown, explanations, additional fields, or any natural language.", - "weight": 100 - } - } - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_01.json b/sleep_assessment/cases/case21/case21_01.json deleted file mode 100644 index 0311b7b446de9f3d2b57a547ad5b942af1034af5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_01.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 139.0-144.0 minutes; B: 96.5-101.5 minutes; C: 331.5-336.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 87.5906727468016, - "tolerance": 4.379533637340081 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 26.623459084838053, - "tolerance": 1.3311729542419029 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 49.97306894714169, - "tolerance": 2.4986534473570847 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_02.json b/sleep_assessment/cases/case21/case21_02.json deleted file mode 100644 index 8cadbaefdc755d3942712c429396bd42208600ca..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_02.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 64.0-69.0 minutes; B: 184.0-189.0 minutes; C: 354.5-359.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "B" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 19.16429604391837, - "tolerance": 0.9582148021959185 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 83.8454977168564, - "tolerance": 4.19227488584282 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 54.972775817497435, - "tolerance": 2.748638790874872 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_03.json b/sleep_assessment/cases/case21/case21_03.json deleted file mode 100644 index 38041aa354f38bec56220c4ad8272d768bf71ec3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_03.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 250.5-255.5 minutes; B: 5.5-10.5 minutes; C: 145.5-150.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "C" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 31.34329516095618, - "tolerance": 1.5671647580478092 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 44.09978377869893, - "tolerance": 2.2049891889349467 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 85.60521495626206, - "tolerance": 4.280260747813103 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_04.json b/sleep_assessment/cases/case21/case21_04.json deleted file mode 100644 index 589d850eec7318252adfc7d02a39edbb34d8bddf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_04.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 152.0-157.0 minutes; B: 196.0-201.0 minutes; C: 377.5-382.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 91.59016371604208, - "tolerance": 4.579508185802104 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 54.75510737858522, - "tolerance": 2.7377553689292613 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 63.232329113094984, - "tolerance": 3.1616164556547495 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_05.json b/sleep_assessment/cases/case21/case21_05.json deleted file mode 100644 index 0318946f7838813482d595a0b7d3c57bba5fd033..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_05.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 101.5-106.5 minutes; B: 194.0-199.0 minutes; C: 115.0-120.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "B" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 35.24534175407361, - "tolerance": 1.7622670877036803 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 88.31902669703837, - "tolerance": 4.415951334851918 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 53.50000902531341, - "tolerance": 2.675000451265671 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_06.json b/sleep_assessment/cases/case21/case21_06.json deleted file mode 100644 index 8becda12836956457db0ec95380e6d6fc3404a0a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_06.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 91.0-96.0 minutes; B: 150.5-155.5 minutes; C: 54.5-59.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "C" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 58.487758791987346, - "tolerance": 2.9243879395993675 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 66.87223181181125, - "tolerance": 3.3436115905905623 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 76.79686764596676, - "tolerance": 3.839843382298338 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_07.json b/sleep_assessment/cases/case21/case21_07.json deleted file mode 100644 index e97155d14504ef42165b9520ef32a6f9e2fd3a0d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_07.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 157.0-162.0 minutes; B: 359.0-364.0 minutes; C: 319.0-324.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 90.89976165674851, - "tolerance": 4.544988082837426 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 61.618960822576305, - "tolerance": 3.0809480411288153 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 62.95006957930629, - "tolerance": 3.1475034789653145 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_08.json b/sleep_assessment/cases/case21/case21_08.json deleted file mode 100644 index 0976b3004e55bcf652eb83b912ff27b27d95e921..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_08.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 60.5-65.5 minutes; B: 232.0-237.0 minutes; C: 440.0-445.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "B" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 51.82226765014284, - "tolerance": 2.5911133825071424 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 96.16343018525266, - "tolerance": 4.808171509262634 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 63.403341361508346, - "tolerance": 3.1701670680754175 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_09.json b/sleep_assessment/cases/case21/case21_09.json deleted file mode 100644 index 94a8542be7f2f0685a4720045651c5077222ac05..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_09.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 496.5-501.5 minutes; B: 379.5-384.5 minutes; C: 43.0-48.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "C" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 46.901744669264325, - "tolerance": 2.3450872334632162 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 60.26390585909406, - "tolerance": 3.0131952929547032 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 84.39028196005106, - "tolerance": 4.219514098002553 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_10.json b/sleep_assessment/cases/case21/case21_10.json deleted file mode 100644 index 617af70ade9a63e2409f88d7aa3271375051afe8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_10.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 348.5-353.5 minutes; B: 138.5-143.5 minutes; C: 42.5-47.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 89.2399238121076, - "tolerance": 4.46199619060538 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 51.65480174696761, - "tolerance": 2.5827400873483803 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 65.51899845038473, - "tolerance": 3.275949922519237 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_11.json b/sleep_assessment/cases/case21/case21_11.json deleted file mode 100644 index 4b2ac0e54c255237b60c3f3d8d4bce2d89245adf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_11.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 469.5-474.5 minutes; B: 53.0-58.0 minutes; C: 128.0-133.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "B" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 15.689854260455554, - "tolerance": 0.7844927130227778 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 93.13518686115162, - "tolerance": 4.6567593430575815 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 51.75967892808275, - "tolerance": 2.5879839464041376 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_12.json b/sleep_assessment/cases/case21/case21_12.json deleted file mode 100644 index 7a20f2278e47cb1a721ad29e5df8ef738d3f2fee..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_12.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 522.0-527.0 minutes; B: 43.0-48.0 minutes; C: 22.0-27.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "C" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 1.2385372169456097, - "tolerance": 0.06192686084728049 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 41.615293543358725, - "tolerance": 2.0807646771679362 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 77.04710423208743, - "tolerance": 3.852355211604372 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_13.json b/sleep_assessment/cases/case21/case21_13.json deleted file mode 100644 index d0d2e7467ee9387abf7bec16e7c26bab2c3854da..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_13.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 100.5-105.5 minutes; B: 377.0-382.0 minutes; C: 413.0-418.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 90.32094560477164, - "tolerance": 4.516047280238582 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 11.840750907955568, - "tolerance": 0.5920375453977784 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 43.09124214961489, - "tolerance": 2.1545621074807446 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_14.json b/sleep_assessment/cases/case21/case21_14.json deleted file mode 100644 index fa25efa58e8d6b8636ff92f6ce51db664354f043..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_14.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 418.5-423.5 minutes; B: 135.5-140.5 minutes; C: 150.0-155.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "B" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 12.27055346030883, - "tolerance": 0.6135276730154415 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 92.56927964330413, - "tolerance": 4.628463982165207 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 33.346197460620544, - "tolerance": 1.6673098730310274 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_15.json b/sleep_assessment/cases/case21/case21_15.json deleted file mode 100644 index 2f8746f881159ae4f838faf0cdb33352d759e8c8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_15.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 489.0-494.0 minutes; B: 140.0-145.0 minutes; C: 105.0-110.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "C" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 27.673189351111194, - "tolerance": 1.3836594675555598 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 43.46777410870381, - "tolerance": 2.1733887054351904 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 86.80179483765484, - "tolerance": 4.340089741882742 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_16.json b/sleep_assessment/cases/case21/case21_16.json deleted file mode 100644 index ac48752f459dddf6fcd38037156f4967bffc74ed..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_16.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 132.5-137.5 minutes; B: 489.5-494.5 minutes; C: 162.0-167.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 93.27637253133445, - "tolerance": 4.663818626566722 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 61.45997426567755, - "tolerance": 3.072998713283878 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 64.66326311853447, - "tolerance": 3.2331631559267238 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_17.json b/sleep_assessment/cases/case21/case21_17.json deleted file mode 100644 index 6c3c7e50b99470c2b89db614a3ec8e7d3e93c8ac..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_17.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 6.0-11.0 minutes; B: 47.5-52.5 minutes; C: 64.0-69.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "B" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 23.19850896800388, - "tolerance": 1.159925448400194 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 92.34760687018822, - "tolerance": 4.617380343509411 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 54.11837997230321, - "tolerance": 2.705918998615161 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_18.json b/sleep_assessment/cases/case21/case21_18.json deleted file mode 100644 index 89ba4e996f55c039b0095bfdf41943c875cbb35f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_18.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 384.5-389.5 minutes; B: 180.0-185.0 minutes; C: 78.0-83.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "C" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 58.17958854040366, - "tolerance": 2.908979427020183 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 61.46403534512942, - "tolerance": 3.073201767256471 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 88.29564335221875, - "tolerance": 4.414782167610937 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_19.json b/sleep_assessment/cases/case21/case21_19.json deleted file mode 100644 index 8fef3f3c4d4c480f17c15b7815224268f6fc2293..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_19.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 54.5-59.5 minutes; B: 391.5-396.5 minutes; C: 2.0-7.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 90.73300852862864, - "tolerance": 4.536650426431432 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 55.998864756870795, - "tolerance": 2.79994323784354 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 57.39987466171825, - "tolerance": 2.869993733085913 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_20.json b/sleep_assessment/cases/case21/case21_20.json deleted file mode 100644 index 28707fa57eab46bd9779279b52674cf893b3273c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_20.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 473.0-478.0 minutes; B: 76.0-81.0 minutes; C: 335.0-340.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "B" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 55.67484078891339, - "tolerance": 2.78374203944567 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 95.34960832725586, - "tolerance": 4.767480416362793 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 66.126918150972, - "tolerance": 3.3063459075486 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_21.json b/sleep_assessment/cases/case21/case21_21.json deleted file mode 100644 index fa6830b7a252d4638e17e42e8041dbc1586593b0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_21.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 291.5-296.5 minutes; B: 25.0-30.0 minutes; C: 52.5-57.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "C" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 59.31677355978399, - "tolerance": 2.9658386779891996 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 81.05282741279751, - "tolerance": 4.052641370639876 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 86.88394324697192, - "tolerance": 4.344197162348596 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_22.json b/sleep_assessment/cases/case21/case21_22.json deleted file mode 100644 index 2392dd4b3a78b481943b57493cffe7a053fbb357..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_22.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 315.0-320.0 minutes; B: 296.0-301.0 minutes; C: 224.5-229.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 78.04769500659657, - "tolerance": 3.9023847503298286 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 75.74397205594231, - "tolerance": 3.7871986027971154 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 27.43508758259991, - "tolerance": 1.3717543791299955 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_23.json b/sleep_assessment/cases/case21/case21_23.json deleted file mode 100644 index ddb5b7430a1d2735f25da5823d82775dac6f8320..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_23.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 430.5-435.5 minutes; B: 65.0-70.0 minutes; C: 12.0-17.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "B" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 59.252773178980036, - "tolerance": 2.962638658949002 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 92.71052166121406, - "tolerance": 4.635526083060703 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 60.27504120405306, - "tolerance": 3.0137520602026533 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_24.json b/sleep_assessment/cases/case21/case21_24.json deleted file mode 100644 index a7478ca1405866fb784b24cd4859a84a442c14a0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_24.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 281.5-286.5 minutes; B: 291.5-296.5 minutes; C: 137.0-142.0 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "C" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 32.48030419404315, - "tolerance": 1.6240152097021576 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 67.38808770844375, - "tolerance": 3.369404385422188 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 89.21449458576754, - "tolerance": 4.460724729288377 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case21/case21_25.json b/sleep_assessment/cases/case21/case21_25.json deleted file mode 100644 index 945329a352f1908dcbe7346bacd4b82460992ecd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case21/case21_25.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 365.5-370.5 minutes; B: 260.0-265.0 minutes; C: 273.5-278.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": , \"delta_ratio_B\": , \"delta_ratio_C\": }", - "metrics": [ - { - "metric_id": "slow_wave_rich_window_accuracy", - "type": "categorical_check", - "target_key": "slow_wave_rich_window", - "weight": 40, - "params": { - "gt_value": "A" - } - }, - { - "metric_id": "delta_ratio_A_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_A", - "weight": 20, - "params": { - "gt_value": 87.95827716935813, - "tolerance": 4.3979138584679065 - } - }, - { - "metric_id": "delta_ratio_B_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_B", - "weight": 20, - "params": { - "gt_value": 45.63446686581834, - "tolerance": 2.281723343290917 - } - }, - { - "metric_id": "delta_ratio_C_accuracy", - "type": "numeric_check", - "target_key": "delta_ratio_C", - "weight": 20, - "params": { - "gt_value": 67.24085984957885, - "tolerance": 3.362042992478943 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_01.json b/sleep_assessment/cases/case22/case22_01.json deleted file mode 100644 index aa2676f2795ab907b97914de980a3cc2e658cae4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_01.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 189.5 to minute 192.5. For this ISRUC file, X1, X3, and X4 are EMG channels. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.9257770939298657e-07, - "tolerance": 1.9257770939298657e-08 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The analyzed event window is 189.5-192.5 minutes. The current window is a true REM segment. The EMG channels are X1, X3, X4. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 1.92577709393e-07; the whole-night EMG RMS is 1.63135693279e-06; the event/whole-night RMS ratio is 0.118048. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_02.json b/sleep_assessment/cases/case22/case22_02.json deleted file mode 100644 index d27f393c777ada3939b3033d2e3168805eb870bf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_02.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 462.0 to minute 465.0. For this ISRUC file, X1, X3, and X4 are EMG channels. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 6.807735444943595e-08, - "tolerance": 6.807735444943596e-09 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The analyzed event window is 462.0-465.0 minutes. The current window is a true REM segment. The EMG channels are X1, X3, X4. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 6.80773544494e-08; the whole-night EMG RMS is 1.04680457673e-06; the event/whole-night RMS ratio is 0.065033. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_03.json b/sleep_assessment/cases/case22/case22_03.json deleted file mode 100644 index 699d88c675a8b6efea4b96a4f2ae0128dad2725c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_03.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 175.0 to minute 178.0. For this ISRUC file, X1, X3, and X4 are EMG channels. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.63447757802915e-07, - "tolerance": 1.6344775780291502e-08 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The analyzed event window is 175.0-178.0 minutes. The current window is a true REM segment. The EMG channels are X1, X3, X4. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 1.63447757803e-07; the whole-night EMG RMS is 9.73054813996e-07; the event/whole-night RMS ratio is 0.167974. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_04.json b/sleep_assessment/cases/case22/case22_04.json deleted file mode 100644 index 1eaf2e246042ff868acc3006f8b41d4c17ebf12c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_04.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 253.0 to minute 256.0. For this ISRUC file, X1, X3, and X4 are EMG channels. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.7294296493802682e-06, - "tolerance": 1.7294296493802683e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. The analyzed event window is 253.0-256.0 minutes. The current window is a true Non-REM segment. The EMG channels are X1, X3, X4. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 1.72942964938e-06; the whole-night EMG RMS is 4.6295173256e-07; the event/whole-night RMS ratio is 3.735659. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_05.json b/sleep_assessment/cases/case22/case22_05.json deleted file mode 100644 index e74016ad0b48eaf13a62845e0733ecb1b96a0ebf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_05.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 143.0 to minute 146.0. For this ISRUC file, X1, X3, and X4 are EMG channels. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.2709966405964798e-06, - "tolerance": 1.27099664059648e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The analyzed event window is 143.0-146.0 minutes. The current window is a true Non-REM segment. The EMG channels are X1, X3, X4. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 1.2709966406e-06; the whole-night EMG RMS is 1.26925715064e-06; the event/whole-night RMS ratio is 1.001370. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_06.json b/sleep_assessment/cases/case22/case22_06.json deleted file mode 100644 index d13ba34e42379d3e712756851b1694becc9dab3a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_06.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 365.5 to minute 368.5. Use the following EMG channel(s): EMG chin. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.2125094878058998e-06, - "tolerance": 1.2125094878059e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_01.edf. The analyzed event window is 365.5-368.5 minutes. The current window is a true REM segment. The EMG channels are EMG chin. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 1.21250948781e-06; the whole-night EMG RMS is 2.89342732424e-06; the event/whole-night RMS ratio is 0.419056. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_07.json b/sleep_assessment/cases/case22/case22_07.json deleted file mode 100644 index 48d6117742a5707487c76ab62037dbea71d8c5dc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_07.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 296.5 to minute 299.5. Use the following EMG channel(s): EMG chin. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.2927010134758717e-06, - "tolerance": 1.2927010134758718e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_02.edf. The analyzed event window is 296.5-299.5 minutes. The current window is a true REM segment. The EMG channels are EMG chin. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 1.29270101348e-06; the whole-night EMG RMS is 3.35097519953e-06; the event/whole-night RMS ratio is 0.385769. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_08.json b/sleep_assessment/cases/case22/case22_08.json deleted file mode 100644 index b746104cf3804148094f9ece957d70d5de528b71..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_08.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 292.5 to minute 295.5. Use the following EMG channel(s): EMG chin. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.45519900392743e-06, - "tolerance": 1.4551990039274303e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_03.edf. The analyzed event window is 292.5-295.5 minutes. The current window is a true REM segment. The EMG channels are EMG chin. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 1.45519900393e-06; the whole-night EMG RMS is 4.77630487829e-06; the event/whole-night RMS ratio is 0.304670. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_09.json b/sleep_assessment/cases/case22/case22_09.json deleted file mode 100644 index aab3868625627a88e9e8a688c54a2a3767b737f0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_09.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 214.5 to minute 217.5. Use the following EMG channel(s): EMG chin. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 5.8366780036124085e-06, - "tolerance": 5.836678003612409e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_04.edf. The analyzed event window is 214.5-217.5 minutes. The current window is a true Non-REM segment. The EMG channels are EMG chin. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 5.83667800361e-06; the whole-night EMG RMS is 2.75387668133e-06; the event/whole-night RMS ratio is 2.119441. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_10.json b/sleep_assessment/cases/case22/case22_10.json deleted file mode 100644 index e4ea0098997c68468a0f16313a974b1a27242a90..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_10.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 22.5 to minute 25.5. Use the following EMG channel(s): EMG chin. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.3648126561724187e-05, - "tolerance": 1.3648126561724189e-06 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_05.edf. The analyzed event window is 22.5-25.5 minutes. The current window is a true Non-REM segment. The EMG channels are EMG chin. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 1.36481265617e-05; the whole-night EMG RMS is 8.68256865068e-06; the event/whole-night RMS ratio is 1.571900. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_11.json b/sleep_assessment/cases/case22/case22_11.json deleted file mode 100644 index 4c7f24c521180d7ae17ec8ef2f97e9a588cf4561..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_11.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 241.5 to minute 244.5. Use the following EMG channel(s): EMG. Select the chin EMG/EMG channel(s), apply a 10-50Hz bandpass filter appropriate for the 125Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.158347861593798e-06, - "tolerance": 1.1583478615937979e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The analyzed event window is 241.5-244.5 minutes. The current window is a true REM segment. The EMG channels are EMG. The bandpass filter is 10-50Hz. The ground-truth event-window EMG RMS is 1.15834786159e-06; the whole-night EMG RMS is 8.79580293825e-06; the event/whole-night RMS ratio is 0.131693. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_12.json b/sleep_assessment/cases/case22/case22_12.json deleted file mode 100644 index 1f0e104fed826a04be47dddb2d47ecbd7090d08b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_12.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 152.0 to minute 155.0. Use the following EMG channel(s): EMG. Select the chin EMG/EMG channel(s), apply a 10-50Hz bandpass filter appropriate for the 125Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.3714052883906417e-06, - "tolerance": 1.3714052883906417e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The analyzed event window is 152.0-155.0 minutes. The current window is a true REM segment. The EMG channels are EMG. The bandpass filter is 10-50Hz. The ground-truth event-window EMG RMS is 1.37140528839e-06; the whole-night EMG RMS is 8.80517358953e-06; the event/whole-night RMS ratio is 0.155750. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_13.json b/sleep_assessment/cases/case22/case22_13.json deleted file mode 100644 index 8f838715e9428b59b1c682f1527013ed16c30614..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_13.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 448.0 to minute 451.0. Use the following EMG channel(s): EMG. Select the chin EMG/EMG channel(s), apply a 10-50Hz bandpass filter appropriate for the 125Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.047611254669687e-06, - "tolerance": 1.047611254669687e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The analyzed event window is 448.0-451.0 minutes. The current window is a true REM segment. The EMG channels are EMG. The bandpass filter is 10-50Hz. The ground-truth event-window EMG RMS is 1.04761125467e-06; the whole-night EMG RMS is 6.41498034226e-06; the event/whole-night RMS ratio is 0.163307. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_14.json b/sleep_assessment/cases/case22/case22_14.json deleted file mode 100644 index acd25789c1a2e00e26013e95d8ac69bca386e693..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_14.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 41.0 to minute 44.0. Use the following EMG channel(s): EMG. Select the chin EMG/EMG channel(s), apply a 10-50Hz bandpass filter appropriate for the 125Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 5.529400258158198e-06, - "tolerance": 5.529400258158197e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The analyzed event window is 41.0-44.0 minutes. The current window is a true Non-REM segment. The EMG channels are EMG. The bandpass filter is 10-50Hz. The ground-truth event-window EMG RMS is 5.52940025816e-06; the whole-night EMG RMS is 4.92841082717e-06; the event/whole-night RMS ratio is 1.121944. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_15.json b/sleep_assessment/cases/case22/case22_15.json deleted file mode 100644 index 7d3626adf4d3732d37fded8086a3d91bb251c196..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_15.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 84.5 to minute 87.5. Use the following EMG channel(s): EMG. Select the chin EMG/EMG channel(s), apply a 10-50Hz bandpass filter appropriate for the 125Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 5.1888570663235356e-06, - "tolerance": 5.188857066323536e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The analyzed event window is 84.5-87.5 minutes. The current window is a true Non-REM segment. The EMG channels are EMG. The bandpass filter is 10-50Hz. The ground-truth event-window EMG RMS is 5.18885706632e-06; the whole-night EMG RMS is 5.07710627434e-06; the event/whole-night RMS ratio is 1.022011. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_16.json b/sleep_assessment/cases/case22/case22_16.json deleted file mode 100644 index b91f0b51dc71473e1ebe90428bde1c71b3757bc6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_16.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 158.5 to minute 161.5. Use the following EMG channel(s): EMG Chin1, EMG Chin2, EMG Chin3. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.3221747030390449e-06, - "tolerance": 1.322174703039045e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_01.edf. The analyzed event window is 158.5-161.5 minutes. The current window is a true REM segment. The EMG channels are EMG Chin1, EMG Chin2, EMG Chin3. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 1.32217470304e-06; the whole-night EMG RMS is 8.00297894656e-06; the event/whole-night RMS ratio is 0.165210. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_17.json b/sleep_assessment/cases/case22/case22_17.json deleted file mode 100644 index 8865f58c6ff90f9982ab35e456a6e134dc962a0c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_17.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 74.5 to minute 77.5. Use the following EMG channel(s): EMG Chin1, EMG Chin2, EMG Chin3. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 8.410101287302534e-07, - "tolerance": 8.410101287302535e-08 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_02.edf. The analyzed event window is 74.5-77.5 minutes. The current window is a true REM segment. The EMG channels are EMG Chin1, EMG Chin2, EMG Chin3. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 8.4101012873e-07; the whole-night EMG RMS is 3.85566057862e-06; the event/whole-night RMS ratio is 0.218123. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_18.json b/sleep_assessment/cases/case22/case22_18.json deleted file mode 100644 index 7cab5324599dc75968f82ee51fdc062aec8a9c3c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_18.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 356.5 to minute 359.5. Use the following EMG channel(s): EMG Chin1, EMG Chin2, EMG Chin3. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 8.634979564874514e-07, - "tolerance": 8.634979564874515e-08 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_03.edf. The analyzed event window is 356.5-359.5 minutes. The current window is a true REM segment. The EMG channels are EMG Chin1, EMG Chin2, EMG Chin3. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 8.63497956487e-07; the whole-night EMG RMS is 4.78397922626e-06; the event/whole-night RMS ratio is 0.180498. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_19.json b/sleep_assessment/cases/case22/case22_19.json deleted file mode 100644 index a409cad407b77fe3bc22b7c82335fd5db0954cb5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_19.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 42.0 to minute 45.0. Use the following EMG channel(s): EMG Chin1, EMG Chin2, EMG Chin3. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.8436884876285053e-05, - "tolerance": 1.8436884876285053e-06 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_04.edf. The analyzed event window is 42.0-45.0 minutes. The current window is a true Non-REM segment. The EMG channels are EMG Chin1, EMG Chin2, EMG Chin3. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 1.84368848763e-05; the whole-night EMG RMS is 6.91086656454e-06; the event/whole-night RMS ratio is 2.667811. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_20.json b/sleep_assessment/cases/case22/case22_20.json deleted file mode 100644 index 651588865cd5d45adc94280d20f2fb8445e55567..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_20.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 93.5 to minute 96.5. Use the following EMG channel(s): EMG Chin1, EMG Chin2, EMG Chin3. Select the chin EMG/EMG channel(s), apply a 10-100Hz bandpass filter appropriate for the 256Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 2.226726128501951e-05, - "tolerance": 2.226726128501951e-06 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_05.edf. The analyzed event window is 93.5-96.5 minutes. The current window is a true Non-REM segment. The EMG channels are EMG Chin1, EMG Chin2, EMG Chin3. The bandpass filter is 10-100Hz. The ground-truth event-window EMG RMS is 2.2267261285e-05; the whole-night EMG RMS is 7.47323196746e-06; the event/whole-night RMS ratio is 2.979603. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_21.json b/sleep_assessment/cases/case22/case22_21.json deleted file mode 100644 index 254776b7c2f8eb4c04385fa889aa84a5f72a8f81..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_21.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 239.5 to minute 242.5. Use the following EMG channel(s): Chin1-Chin2. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 6.9547744842769485e-06, - "tolerance": 6.954774484276949e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_01.edf. The analyzed event window is 239.5-242.5 minutes. The current window is a true REM segment. The EMG channels are Chin1-Chin2. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 6.95477448428e-06; the whole-night EMG RMS is 1.97842728729e-05; the event/whole-night RMS ratio is 0.351530. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_22.json b/sleep_assessment/cases/case22/case22_22.json deleted file mode 100644 index 22a899d5e9185b46e9cde3de13322daf2a39e91e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_22.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 138.0 to minute 141.0. Use the following EMG channel(s): Chin1-Chin2. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 9.421387291713087e-06, - "tolerance": 9.421387291713087e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_02.edf. The analyzed event window is 138.0-141.0 minutes. The current window is a true Non-REM segment. The EMG channels are Chin1-Chin2. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 9.42138729171e-06; the whole-night EMG RMS is 1.66585452873e-05; the event/whole-night RMS ratio is 0.565559. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_23.json b/sleep_assessment/cases/case22/case22_23.json deleted file mode 100644 index 1a508ff5470e81f539507a4a6d837b2bde6df9a1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_23.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 261.0 to minute 264.0. Use the following EMG channel(s): Chin1-Chin2. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 1.1150316310254854e-05, - "tolerance": 1.1150316310254855e-06 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_03.edf. The analyzed event window is 261.0-264.0 minutes. The current window is a true Non-REM segment. The EMG channels are Chin1-Chin2. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 1.11503163103e-05; the whole-night EMG RMS is 2.05199197901e-05; the event/whole-night RMS ratio is 0.543390. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: not REM-like. The correct response must judge the window as non-REM-like or not consistent with a REM-like pattern. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_24.json b/sleep_assessment/cases/case22/case22_24.json deleted file mode 100644 index 392e4cdb0538110902cdd6c7434902e1a8d9d7bc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_24.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 66.5 to minute 69.5. Use the following EMG channel(s): Chin1-Chin2. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 6.68070263010799e-06, - "tolerance": 6.680702630107991e-07 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_04.edf. The analyzed event window is 66.5-69.5 minutes. The current window is a true REM segment. The EMG channels are Chin1-Chin2. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 6.68070263011e-06; the whole-night EMG RMS is 7.96777951305e-05; the event/whole-night RMS ratio is 0.083846. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case22/case22_25.json b/sleep_assessment/cases/case22/case22_25.json deleted file mode 100644 index eaa2004cbb748dfe057809525978d8c5f679e523..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case22/case22_25.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please analyze the provided sleep file in the time window from minute 371.5 to minute 374.5. Use the following EMG channel(s): Chin1-Chin2. Select the chin EMG/EMG channel(s), apply a 10-80Hz bandpass filter appropriate for the 200Hz sampling rate, and compute the mean chin EMG RMS value in this event window. Also compute the whole-night EMG RMS mean using the same EMG channel(s) and filter. Compare the event-window RMS with the whole-night RMS, determine whether the event window follows a REM-like pattern, and give a brief analysis. Clearly report the event-window EMG RMS, the whole-night EMG RMS, and the final REM-like or non-REM-like judgment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the event-window chin EMG RMS value and preserve the complete report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"event_emg_rms\" and \"full_text\".\n5. \"event_emg_rms\" must be a FLOAT or null. Do not include units.\n6. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n7. If the event-window EMG RMS value is missing or unclear, set \"event_emg_rms\" to null.\n\n### OUTPUT TEMPLATE\n{\"event_emg_rms\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "event_chin_emg_rms_accuracy", - "type": "numeric_check", - "target_key": "event_emg_rms", - "weight": 40, - "params": { - "gt_value": 8.298027416980718e-07, - "tolerance": 8.298027416980719e-08 - } - }, - { - "metric_id": "rem_like_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_05.edf. The analyzed event window is 371.5-374.5 minutes. The current window is a true REM segment. The EMG channels are Chin1-Chin2. The bandpass filter is 10-80Hz. The ground-truth event-window EMG RMS is 8.29802741698e-07; the whole-night EMG RMS is 5.50541279185e-06; the event/whole-night RMS ratio is 0.150725. The REM-like threshold is event RMS <= 0.8 * whole-night RMS. The correct final REM-like conclusion is: REM-like. The correct response must state that the event-window EMG RMS is significantly lower than the whole-night EMG RMS and must judge the window as REM or REM-like. Mark the response as correct only if it uses the RMS comparison consistently and gives the correct REM-like versus non-REM-like conclusion. If the response gives the opposite conclusion, ignores the RMS comparison, claims REM-like for a true Non-REM segment, or contradicts these rules, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_01.json b/sleep_assessment/cases/case23/case23_01.json deleted file mode 100644 index 85e3076ce83f1217994510dc6ba59a57d9dd7228..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_01.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 67.33469949370581, - "tolerance": 3.366734974685291 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 56.33204300356318, - "tolerance": 2.8166021501781593 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The ground-truth first-half delta relative energy is 67.3346994937%, and the ground-truth second-half delta relative energy is 56.3320430036%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_02.json b/sleep_assessment/cases/case23/case23_02.json deleted file mode 100644 index 1b079b30a91c61ed19fdbc7df16df64288d65c68..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_02.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 59.74017061524712, - "tolerance": 2.987008530762356 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 61.552457872627485, - "tolerance": 3.0776228936313745 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The ground-truth first-half delta relative energy is 59.7401706152%, and the ground-truth second-half delta relative energy is 61.5524578726%. Based on these values, delta activity is stronger in the second half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: not consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_03.json b/sleep_assessment/cases/case23/case23_03.json deleted file mode 100644 index da5eaeaf66c23fa2b99641f8d0942c90b0d6eb9c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_03.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 64.41381533056132, - "tolerance": 3.220690766528066 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 54.92228225706158, - "tolerance": 2.7461141128530793 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The ground-truth first-half delta relative energy is 64.4138153306%, and the ground-truth second-half delta relative energy is 54.9222822571%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_04.json b/sleep_assessment/cases/case23/case23_04.json deleted file mode 100644 index 3811e8824b3b500ad03ee62eb373372a2d01ad60..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_04.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 73.64779024300691, - "tolerance": 3.682389512150346 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 64.5161251475125, - "tolerance": 3.225806257375625 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. The ground-truth first-half delta relative energy is 73.647790243%, and the ground-truth second-half delta relative energy is 64.5161251475%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_05.json b/sleep_assessment/cases/case23/case23_05.json deleted file mode 100644 index f8a131ee59cdf2e99758ec25f39834326a921504..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_05.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 60.99836719455589, - "tolerance": 3.0499183597277946 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 58.41735443656893, - "tolerance": 2.9208677218284467 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The ground-truth first-half delta relative energy is 60.9983671946%, and the ground-truth second-half delta relative energy is 58.4173544366%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_06.json b/sleep_assessment/cases/case23/case23_06.json deleted file mode 100644 index d1c54249bee5ef320616a579f2a58c7dae52a650..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_06.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 53.2454651557235, - "tolerance": 2.6622732577861754 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 53.89565679480508, - "tolerance": 2.6947828397402542 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_01.edf. The ground-truth first-half delta relative energy is 53.2454651557%, and the ground-truth second-half delta relative energy is 53.8956567948%. Based on these values, delta activity is stronger in the second half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: not consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_07.json b/sleep_assessment/cases/case23/case23_07.json deleted file mode 100644 index 43b3441ca374ebfa139738ba341c2910f7ce4f57..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_07.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 68.2079546881756, - "tolerance": 3.4103977344087806 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 64.71481590110388, - "tolerance": 3.235740795055194 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_02.edf. The ground-truth first-half delta relative energy is 68.2079546882%, and the ground-truth second-half delta relative energy is 64.7148159011%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_08.json b/sleep_assessment/cases/case23/case23_08.json deleted file mode 100644 index d7260248ed64ee5911ee640a945695b0085f3517..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_08.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 63.84305095250431, - "tolerance": 3.192152547625216 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 59.32618439168844, - "tolerance": 2.9663092195844225 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_03.edf. The ground-truth first-half delta relative energy is 63.8430509525%, and the ground-truth second-half delta relative energy is 59.3261843917%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_09.json b/sleep_assessment/cases/case23/case23_09.json deleted file mode 100644 index a53dc933e042c332d9d5fc57711557b764131ff3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_09.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 59.29554008793102, - "tolerance": 2.9647770043965513 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 52.63817079488876, - "tolerance": 2.631908539744438 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_04.edf. The ground-truth first-half delta relative energy is 59.2955400879%, and the ground-truth second-half delta relative energy is 52.6381707949%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_10.json b/sleep_assessment/cases/case23/case23_10.json deleted file mode 100644 index 8b765da5bf4acf78ab3c5c5cc97968d28e898e91..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_10.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 62.38383614739112, - "tolerance": 3.1191918073695564 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 63.20857081319707, - "tolerance": 3.160428540659854 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_05.edf. The ground-truth first-half delta relative energy is 62.3838361474%, and the ground-truth second-half delta relative energy is 63.2085708132%. Based on these values, delta activity is stronger in the second half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: not consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_11.json b/sleep_assessment/cases/case23/case23_11.json deleted file mode 100644 index 1c03f5a334e898c9ca79b226538c6ec054df283e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_11.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 54.94309662111741, - "tolerance": 2.747154831055871 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 31.940083384351215, - "tolerance": 1.597004169217561 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The ground-truth first-half delta relative energy is 54.9430966211%, and the ground-truth second-half delta relative energy is 31.9400833844%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_12.json b/sleep_assessment/cases/case23/case23_12.json deleted file mode 100644 index d9c413b53256cf1c0cfaae21ec5f646ac74e216a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_12.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 36.49717974900702, - "tolerance": 1.824858987450351 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 6.116601437124146, - "tolerance": 0.3058300718562073 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The ground-truth first-half delta relative energy is 36.497179749%, and the ground-truth second-half delta relative energy is 6.11660143712%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_13.json b/sleep_assessment/cases/case23/case23_13.json deleted file mode 100644 index 8793a5e39e5db783f2146ef0140f940b786561f5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_13.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 54.80242716070924, - "tolerance": 2.7401213580354624 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 62.87760275000422, - "tolerance": 3.1438801375002114 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The ground-truth first-half delta relative energy is 54.8024271607%, and the ground-truth second-half delta relative energy is 62.87760275%. Based on these values, delta activity is stronger in the second half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: not consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_14.json b/sleep_assessment/cases/case23/case23_14.json deleted file mode 100644 index feb624774fcfaef2775a77393c2a9aebc8c74d85..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_14.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 74.60040758551183, - "tolerance": 3.730020379275592 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 59.16313938394403, - "tolerance": 2.9581569691972014 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The ground-truth first-half delta relative energy is 74.6004075855%, and the ground-truth second-half delta relative energy is 59.1631393839%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_15.json b/sleep_assessment/cases/case23/case23_15.json deleted file mode 100644 index 8d7f7e6735fdf2ba43b683bfb0a22425a2822ad2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_15.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 58.7792740401091, - "tolerance": 2.9389637020054553 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 51.92668533423487, - "tolerance": 2.5963342667117435 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The ground-truth first-half delta relative energy is 58.7792740401%, and the ground-truth second-half delta relative energy is 51.9266853342%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_16.json b/sleep_assessment/cases/case23/case23_16.json deleted file mode 100644 index 001fe2bf15efb96494293ab22ca09f6b152e56c7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_16.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 76.73606841909654, - "tolerance": 3.836803420954827 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 67.25899972754054, - "tolerance": 3.362949986377027 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_01.edf. The ground-truth first-half delta relative energy is 76.7360684191%, and the ground-truth second-half delta relative energy is 67.2589997275%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_17.json b/sleep_assessment/cases/case23/case23_17.json deleted file mode 100644 index 7c79dcee26e2b43afba24a50988be0f4a3f06a31..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_17.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 72.53159928810908, - "tolerance": 3.626579964405454 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 63.274320009356465, - "tolerance": 3.1637160004678235 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_02.edf. The ground-truth first-half delta relative energy is 72.5315992881%, and the ground-truth second-half delta relative energy is 63.2743200094%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_18.json b/sleep_assessment/cases/case23/case23_18.json deleted file mode 100644 index dd423f792e06d2fd8153f33d1c0691d4c8dad007..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_18.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 71.80063165302475, - "tolerance": 3.5900315826512377 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 69.30178185761056, - "tolerance": 3.4650890928805285 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_03.edf. The ground-truth first-half delta relative energy is 71.800631653%, and the ground-truth second-half delta relative energy is 69.3017818576%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_19.json b/sleep_assessment/cases/case23/case23_19.json deleted file mode 100644 index d2bbe429a1f1989b99d72f6640601caeaf61aea9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_19.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 75.72108377007636, - "tolerance": 3.7860541885038184 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 62.98382025329574, - "tolerance": 3.149191012664787 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_04.edf. The ground-truth first-half delta relative energy is 75.7210837701%, and the ground-truth second-half delta relative energy is 62.9838202533%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_20.json b/sleep_assessment/cases/case23/case23_20.json deleted file mode 100644 index f9f881562dd4985fea77fa11110d934924de9765..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_20.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 81.66016329954789, - "tolerance": 4.083008164977395 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 65.26062456325221, - "tolerance": 3.2630312281626104 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_05.edf. The ground-truth first-half delta relative energy is 81.6601632995%, and the ground-truth second-half delta relative energy is 65.2606245633%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_21.json b/sleep_assessment/cases/case23/case23_21.json deleted file mode 100644 index f203657178cfaa4eb03e0438b7b78c05169ce8c5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_21.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 66.11556455168528, - "tolerance": 3.3057782275842644 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 61.548092145181144, - "tolerance": 3.0774046072590573 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_01.edf. The ground-truth first-half delta relative energy is 66.1155645517%, and the ground-truth second-half delta relative energy is 61.5480921452%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_22.json b/sleep_assessment/cases/case23/case23_22.json deleted file mode 100644 index 54621e67c3a7e8ce6d40a5b2b92273597a6ee628..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_22.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 51.363191485886006, - "tolerance": 2.5681595742943006 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 68.09016875833315, - "tolerance": 3.4045084379166575 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_02.edf. The ground-truth first-half delta relative energy is 51.3631914859%, and the ground-truth second-half delta relative energy is 68.0901687583%. Based on these values, delta activity is stronger in the second half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: not consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_23.json b/sleep_assessment/cases/case23/case23_23.json deleted file mode 100644 index bf13899660e7b1eb67dd4d1b31b60bd3e14b5229..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_23.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 58.808126294987204, - "tolerance": 2.94040631474936 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 55.85273881278936, - "tolerance": 2.7926369406394684 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_03.edf. The ground-truth first-half delta relative energy is 58.808126295%, and the ground-truth second-half delta relative energy is 55.8527388128%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_24.json b/sleep_assessment/cases/case23/case23_24.json deleted file mode 100644 index 1693264bb075b7c327479d691ce169390a297637..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_24.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 71.07078653093416, - "tolerance": 3.553539326546708 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 56.952817277383325, - "tolerance": 2.8476408638691666 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_04.edf. The ground-truth first-half delta relative energy is 71.0707865309%, and the ground-truth second-half delta relative energy is 56.9528172774%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case23/case23_25.json b/sleep_assessment/cases/case23/case23_25.json deleted file mode 100644 index f09aac83d12dc48b26110249a6a901a94e35f424..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case23/case23_25.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please analyze the provided overnight sleep EEG recording. Select all genuine EEG channels only. Apply a 0.3-40Hz bandpass filter to the selected EEG signals. Split the available recording duration into the first half of the night and the second half of the night by the temporal midpoint. For each half, compute the delta-band relative energy percentage, defined as the 0.5-4Hz band energy divided by the total 0.3-40Hz EEG energy, multiplied by 100. Report the first-half and second-half delta relative energy percentages, state which half shows stronger delta activity, and judge whether this pattern is consistent with classic sleep architecture." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two delta relative energy percentage values and preserve the complete report text for semantic judging:\n1) first-half delta relative energy percentage\n2) second-half delta relative energy percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"first_half_delta_relative_power_percent\", \"second_half_delta_relative_power_percent\", and \"full_text\".\n5. Both delta values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n8. If one delta value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"first_half_delta_relative_power_percent\": , \"second_half_delta_relative_power_percent\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "first_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "first_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 63.27427463871565, - "tolerance": 3.1637137319357826 - } - }, - { - "metric_id": "second_half_delta_relative_power_accuracy", - "type": "numeric_check", - "target_key": "second_half_delta_relative_power_percent", - "weight": 30, - "params": { - "gt_value": 61.09546931626567, - "tolerance": 3.054773465813284 - } - }, - { - "metric_id": "half_night_delta_pattern_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_05.edf. The ground-truth first-half delta relative energy is 63.2742746387%, and the ground-truth second-half delta relative energy is 61.0954693163%. Based on these values, delta activity is stronger in the first half of the night. Given the classic sleep architecture rule that deep sleep/slow-wave activity occurs mainly in the first half of the night, judge whether the agent's reasoning is consistent with the ground-truth delta activity trend and whether its conclusion about classic sleep architecture is correct. If the true delta activity is stronger in the second half of the night, the agent must conclude that the pattern is not consistent with classic sleep architecture. Only when true delta activity is stronger in the first half, and the agent uses that trend to conclude that the pattern is consistent with classic sleep architecture, should the final logic be considered correct. The correct final conclusion for this sample is: consistent with classic sleep architecture. Do not penalize small numeric rounding differences, but mark the response incorrect if it gives the opposite trend, ignores the trend, or treats second-half-stronger or no-first-half-predominance findings as consistent with classic architecture. Output only a valid JSON object with exactly one key named status whose value is true or false. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case24/case24_01.json b/sleep_assessment/cases/case24/case24_01.json deleted file mode 100644 index 4522142a1ef7ac508b068e58d33227ba2c214f19..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_01.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 70.0, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 37.5, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 19.155844155844157, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_02.json b/sleep_assessment/cases/case24/case24_02.json deleted file mode 100644 index 384520e84b51e1b19bd67261572c365f64259357..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_02.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 74.5850622406639, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 21.835883171070932, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 14.186369958275382, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_03.json b/sleep_assessment/cases/case24/case24_03.json deleted file mode 100644 index 40714a62bf54a4e604953bd66651bb6cd7b95130..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_03.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 86.00212089077412, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 21.331689272503084, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 27.990135635018497, - "tolerance": 6.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_04.json b/sleep_assessment/cases/case24/case24_04.json deleted file mode 100644 index 0d1a2220e4e239434ac2c0857989f914ea3204c0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_04.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 97.09241952232607, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 22.887700534759357, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 24.59893048128342, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_05.json b/sleep_assessment/cases/case24/case24_05.json deleted file mode 100644 index 183e2965a55c01cbf11b679bf3301f2af9192e63..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_05.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 66.17142857142858, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 28.324697754749568, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 7.253886010362694, - "tolerance": 4.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_06.json b/sleep_assessment/cases/case24/case24_06.json deleted file mode 100644 index 8c5a9c382b9679114c9f8edfca8781c30449cb06..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_06.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 82.31850117096019, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 3.271692745376956, - "tolerance": 4.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 20.056899004267425, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_07.json b/sleep_assessment/cases/case24/case24_07.json deleted file mode 100644 index 25e9ebe043e3a5aa53c0f95847dd1a1f09fcc3b1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_07.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 90.42056074766356, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 28.940568475452196, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 22.48062015503876, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_08.json b/sleep_assessment/cases/case24/case24_08.json deleted file mode 100644 index a6e541aa6025debe3a7940b21ba12275dd59e3d5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_08.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 57.75681341719078, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 27.22323049001815, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 27.586206896551722, - "tolerance": 6.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_09.json b/sleep_assessment/cases/case24/case24_09.json deleted file mode 100644 index b21c0a77f189bfdea2dfb57f3cbe12b81e130331..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_09.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 92.1259842519685, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 6.837606837606837, - "tolerance": 4.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 26.38888888888889, - "tolerance": 6.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_10.json b/sleep_assessment/cases/case24/case24_10.json deleted file mode 100644 index da4c2ef86831b18d6acdb51406992618199531c4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_10.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 79.04066736183525, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 19.65699208443272, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 16.75461741424802, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_11.json b/sleep_assessment/cases/case24/case24_11.json deleted file mode 100644 index 8bbc818610a0f2fd46437b3eb7819f343a99e66a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_11.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 69.28044280442805, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 19.30758988015979, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 13.581890812250332, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_12.json b/sleep_assessment/cases/case24/case24_12.json deleted file mode 100644 index 7be9b85cecdce207f91e3de03a7d13de9fe8c304..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_12.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 33.734939759036145, - "tolerance": 10.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 16.75824175824176, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 16.75824175824176, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_13.json b/sleep_assessment/cases/case24/case24_13.json deleted file mode 100644 index 90814a0b6599c8ece09072c538196ee455c38a04..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_13.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 68.35081029551954, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 42.81729428172943, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 11.99442119944212, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_14.json b/sleep_assessment/cases/case24/case24_14.json deleted file mode 100644 index 117c0085e5b79ae73c6bebf46332b7b3e5928f33..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_14.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 68.8, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 52.325581395348834, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 15.282392026578073, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_15.json b/sleep_assessment/cases/case24/case24_15.json deleted file mode 100644 index 1159f13faa986ecc2deba7d370d586018803ef70..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_15.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 68.26568265682657, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 13.378378378378379, - "tolerance": 4.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 12.297297297297296, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_16.json b/sleep_assessment/cases/case24/case24_16.json deleted file mode 100644 index ca0ef066da4f63298486a6884c790ee251896c61..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_16.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 93.1547619047619, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 15.548455804046858, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 19.275825346112885, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_17.json b/sleep_assessment/cases/case24/case24_17.json deleted file mode 100644 index b228f3aa1b153c1a066550075cea103f2bab3c2f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_17.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 89.08582089552239, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 18.219895287958114, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 25.340314136125656, - "tolerance": 6.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_18.json b/sleep_assessment/cases/case24/case24_18.json deleted file mode 100644 index 15dad6ee729f7b57474eb04cccbec010f4722603..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_18.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 94.55497382198953, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 13.732004429678849, - "tolerance": 4.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 29.678848283499445, - "tolerance": 6.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_19.json b/sleep_assessment/cases/case24/case24_19.json deleted file mode 100644 index b00bc66ecda782c2b63ded49c48c6ef34640614a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_19.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 98.79385964912281, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 7.103218645948946, - "tolerance": 4.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 27.19200887902331, - "tolerance": 6.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_20.json b/sleep_assessment/cases/case24/case24_20.json deleted file mode 100644 index 00cb79be3ba90e596944b24bb8069bfdbd00e069..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_20.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 95.0420954162769, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 19.19291338582677, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 27.263779527559056, - "tolerance": 6.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_21.json b/sleep_assessment/cases/case24/case24_21.json deleted file mode 100644 index cb889e9bba4c7471cf02cdf4b97e2728fd28e39d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_21.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 56.56804733727811, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 15.06276150627615, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 16.94560669456067, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_22.json b/sleep_assessment/cases/case24/case24_22.json deleted file mode 100644 index 30f1af3359ff5034c19cf14ad1091b25c51e6ca2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_22.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 59.2020592020592, - "tolerance": 8.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 4.565217391304348, - "tolerance": 4.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 0.21739130434782608, - "tolerance": 4.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_23.json b/sleep_assessment/cases/case24/case24_23.json deleted file mode 100644 index 8828fb52d954049fc2f4f401f0a0f763bcf281f1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_23.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 46.21676891615542, - "tolerance": 10.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 13.716814159292035, - "tolerance": 4.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 0.22123893805309736, - "tolerance": 4.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_24.json b/sleep_assessment/cases/case24/case24_24.json deleted file mode 100644 index 03198a7dd7dd12bb3a693524be42fe3303ee4f4b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_24.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 90.40284360189574, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 22.542595019659238, - "tolerance": 5.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 11.795543905635649, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case24/case24_25.json b/sleep_assessment/cases/case24/case24_25.json deleted file mode 100644 index 439377677242d20383d5c3f4f3b6c535c3c0d959..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case24/case24_25.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please use the provided overnight sleep file to perform automatic sleep staging for the entire recording. Based on the resulting sleep-stage sequence, report three final percentage values: sleep efficiency (SE), the percentage of total sleep time spent in N3 deep sleep, and the percentage of total sleep time spent in REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final percentage values from the agent's report:\n1) sleep efficiency (SE)\n2) the percentage of total sleep time spent in N3 deep sleep\n3) the percentage of total sleep time spent in REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"sleep_efficiency_percent\", \"n3_sleep_ratio_percent\", and \"rem_sleep_ratio_percent\".\n5. All three values must be FLOAT percentages or null. Do not include percent signs.\n6. If the report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"sleep_efficiency_percent\": , \"n3_sleep_ratio_percent\": , \"rem_sleep_ratio_percent\": }", - "metrics": [ - { - "metric_id": "sleep_efficiency_accuracy", - "type": "numeric_check", - "target_key": "sleep_efficiency_percent", - "weight": 40, - "params": { - "gt_value": 93.84775808133472, - "tolerance": 5.0 - } - }, - { - "metric_id": "n3_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "n3_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 11.0, - "tolerance": 4.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 30, - "params": { - "gt_value": 11.333333333333334, - "tolerance": 5.0 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case25/case25_01.json b/sleep_assessment/cases/case25/case25_01.json deleted file mode 100644 index b0a47a0b858a42971312388de138097dcc5cc767..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_01.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 181.0 to minute 181.5. Evaluate the claim that the segment's dominant EEG frequency band is alpha and that its sleep stage is R. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 181.0 to minute 181.5, the dominant EEG frequency band is alpha, and the sleep stage is R. Ground truth: the true dominant EEG frequency band is theta, and the true sleep stage is R. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_02.json b/sleep_assessment/cases/case25/case25_02.json deleted file mode 100644 index be2591c4e578122bd301e431a965c0ccd5ae4f5e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_02.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 65.0 to minute 65.5. Evaluate the claim that the segment's dominant EEG frequency band is delta and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 65.0 to minute 65.5, the dominant EEG frequency band is delta, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is alpha, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_03.json b/sleep_assessment/cases/case25/case25_03.json deleted file mode 100644 index 7289f2e8c60cec009b971ca07131349cea95f060..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_03.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 251.0 to minute 251.5. Evaluate the claim that the segment's dominant EEG frequency band is alpha and that its sleep stage is N3. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 251.0 to minute 251.5, the dominant EEG frequency band is alpha, and the sleep stage is N3. Ground truth: the true dominant EEG frequency band is alpha, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_04.json b/sleep_assessment/cases/case25/case25_04.json deleted file mode 100644 index 16b2fb09e0b26491e753104c681796828dbf03ed..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_04.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 147.5 to minute 148.5. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is N3. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 147.5 to minute 148.5, the dominant EEG frequency band is theta, and the sleep stage is N3. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_05.json b/sleep_assessment/cases/case25/case25_05.json deleted file mode 100644 index 5fff28064442024a4bb252e452d973f3ecddf584..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_05.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 415.5 to minute 416.0. Evaluate the claim that the segment's dominant EEG frequency band is beta and that its sleep stage is N3. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 415.5 to minute 416.0, the dominant EEG frequency band is beta, and the sleep stage is N3. Ground truth: the true dominant EEG frequency band is beta, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_06.json b/sleep_assessment/cases/case25/case25_06.json deleted file mode 100644 index daeaba2c994c32457275c05314bf52c5fd8f4af9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_06.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 233.5 to minute 234.5. Evaluate the claim that the segment's dominant EEG frequency band is delta and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_01.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 233.5 to minute 234.5, the dominant EEG frequency band is delta, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_07.json b/sleep_assessment/cases/case25/case25_07.json deleted file mode 100644 index 106d03e97994b17261182e57258b848a09516c72..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_07.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 161.0 to minute 162.0. Evaluate the claim that the segment's dominant EEG frequency band is alpha and that its sleep stage is N3. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_02.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 161.0 to minute 162.0, the dominant EEG frequency band is alpha, and the sleep stage is N3. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_08.json b/sleep_assessment/cases/case25/case25_08.json deleted file mode 100644 index 19bee5bc134a7d5f391fe1a999bd095160c887e1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_08.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 235.5 to minute 237.0. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is R. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_03.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 235.5 to minute 237.0, the dominant EEG frequency band is theta, and the sleep stage is R. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: dominant_band, sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_09.json b/sleep_assessment/cases/case25/case25_09.json deleted file mode 100644 index b9d955e530d1c0aeddd4b050545cee40c2c8c5e8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_09.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 504.5 to minute 505.0. Evaluate the claim that the segment's dominant EEG frequency band is delta and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_04.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 504.5 to minute 505.0, the dominant EEG frequency band is delta, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is beta, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_10.json b/sleep_assessment/cases/case25/case25_10.json deleted file mode 100644 index 4411b2b2b8640f6242f5ca1efe8a7d1dcdcd2c0a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_10.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 144.0 to minute 144.5. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_05.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 144.0 to minute 144.5, the dominant EEG frequency band is theta, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is theta, and the true sleep stage is N1. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_11.json b/sleep_assessment/cases/case25/case25_11.json deleted file mode 100644 index 1397433003fd8f77b2b40dfcd30e775410580278..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_11.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 6.0 to minute 6.5. Evaluate the claim that the segment's dominant EEG frequency band is alpha and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 6.0 to minute 6.5, the dominant EEG frequency band is alpha, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is beta, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_12.json b/sleep_assessment/cases/case25/case25_12.json deleted file mode 100644 index 1eea095d7a9d55faa4e804efb802cb17641579e6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_12.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 87.5 to minute 88.0. Evaluate the claim that the segment's dominant EEG frequency band is alpha and that its sleep stage is R. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 87.5 to minute 88.0, the dominant EEG frequency band is alpha, and the sleep stage is R. Ground truth: the true dominant EEG frequency band is alpha, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_13.json b/sleep_assessment/cases/case25/case25_13.json deleted file mode 100644 index 4269a0b63b4c2c3cc85b75ea29c4fc6fde49ee8f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_13.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 439.0 to minute 439.5. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is N1. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 439.0 to minute 439.5, the dominant EEG frequency band is theta, and the sleep stage is N1. Ground truth: the true dominant EEG frequency band is alpha, and the true sleep stage is N1. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_14.json b/sleep_assessment/cases/case25/case25_14.json deleted file mode 100644 index 729ffa4161532b402716513927ad4e5d7bf574f6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_14.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 150.5 to minute 151.0. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 150.5 to minute 151.0, the dominant EEG frequency band is theta, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is theta, and the true sleep stage is R. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_15.json b/sleep_assessment/cases/case25/case25_15.json deleted file mode 100644 index 35a7a9fe635ede7753a3b1dc014100170c0b0e22..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_15.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 54.0 to minute 55.0. Evaluate the claim that the segment's dominant EEG frequency band is beta and that its sleep stage is N2. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 54.0 to minute 55.0, the dominant EEG frequency band is beta, and the sleep stage is N2. Ground truth: the true dominant EEG frequency band is beta, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_16.json b/sleep_assessment/cases/case25/case25_16.json deleted file mode 100644 index 2a9b7e764e9cce1f948713e38e1010f873073a1b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_16.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 337.5 to minute 338.5. Evaluate the claim that the segment's dominant EEG frequency band is delta and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_01.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 337.5 to minute 338.5, the dominant EEG frequency band is delta, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_17.json b/sleep_assessment/cases/case25/case25_17.json deleted file mode 100644 index f17d5ed24e08dac37251c1e75f020ef975e5e488..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_17.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 517.5 to minute 518.0. Evaluate the claim that the segment's dominant EEG frequency band is beta and that its sleep stage is N1. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_02.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 517.5 to minute 518.0, the dominant EEG frequency band is beta, and the sleep stage is N1. Ground truth: the true dominant EEG frequency band is theta, and the true sleep stage is N1. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_18.json b/sleep_assessment/cases/case25/case25_18.json deleted file mode 100644 index b8d0ad03ace23f316c2dfefb64e374201213a395..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_18.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 78.0 to minute 79.0. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is N3. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_03.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 78.0 to minute 79.0, the dominant EEG frequency band is theta, and the sleep stage is N3. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_19.json b/sleep_assessment/cases/case25/case25_19.json deleted file mode 100644 index 95a1aec0454f451ca319628f282a9af0a49f9cca..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_19.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 57.5 to minute 58.5. Evaluate the claim that the segment's dominant EEG frequency band is delta and that its sleep stage is R. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_04.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 57.5 to minute 58.5, the dominant EEG frequency band is delta, and the sleep stage is R. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_20.json b/sleep_assessment/cases/case25/case25_20.json deleted file mode 100644 index a29f8bfaf0e37e804976c308f00ba3e8aaf6182e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_20.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 80.0 to minute 81.0. Evaluate the claim that the segment's dominant EEG frequency band is alpha and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_05.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 80.0 to minute 81.0, the dominant EEG frequency band is alpha, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: dominant_band, sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_21.json b/sleep_assessment/cases/case25/case25_21.json deleted file mode 100644 index 4a960f710f7a634745e6667c789821e66f697293..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_21.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 8.5 to minute 9.0. Evaluate the claim that the segment's dominant EEG frequency band is alpha and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_01.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 8.5 to minute 9.0, the dominant EEG frequency band is alpha, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N1. The provided conclusion is invalid. The incorrect component(s) are: dominant_band, sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_22.json b/sleep_assessment/cases/case25/case25_22.json deleted file mode 100644 index bc2b51987cee8947e2172f40eaa615fb489fd60b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_22.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 131.5 to minute 132.0. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_02.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 131.5 to minute 132.0, the dominant EEG frequency band is theta, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_23.json b/sleep_assessment/cases/case25/case25_23.json deleted file mode 100644 index b576ad7b93a0965c6961231b37d3dd0604d85437..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_23.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 452.0 to minute 452.5. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is W. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_03.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 452.0 to minute 452.5, the dominant EEG frequency band is theta, and the sleep stage is W. Ground truth: the true dominant EEG frequency band is alpha, and the true sleep stage is W. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_24.json b/sleep_assessment/cases/case25/case25_24.json deleted file mode 100644 index 87fad54b624b59715d314ec9ddc5acd5c9fe9e92..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_24.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 121.5 to minute 124.5. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is N1. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_04.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 121.5 to minute 124.5, the dominant EEG frequency band is theta, and the sleep stage is N1. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: dominant_band, sleep_stage. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case25/case25_25.json b/sleep_assessment/cases/case25/case25_25.json deleted file mode 100644 index 5d5056c1e1b42ac0361b33ea417c30876b0d7ec1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case25/case25_25.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please analyze the provided sleep file and examine the EEG segment from minute 364.5 to minute 367.5. Evaluate the claim that the segment's dominant EEG frequency band is theta and that its sleep stage is N3. Report whether the claim is supported, and provide the dominant frequency band and sleep stage you determine for this segment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to preserve the agent's answer for semantic judging.\n\n### TASK\nExtract the full substantive text of the agent's report, including its validity judgement, identified errors, and corrected answer if present.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The key must be EXACTLY \"full_text\".\n5. If the report is empty or no substantive answer is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "false_conclusion_verification_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_05.edf. The agent was asked to verify this conclusion: in the EEG segment from minute 364.5 to minute 367.5, the dominant EEG frequency band is theta, and the sleep stage is N3. Ground truth: the true dominant EEG frequency band is delta, and the true sleep stage is N3. The provided conclusion is invalid. The incorrect component(s) are: dominant_band. Score the response in two parts. Give 0.5 points if the response clearly recognizes that the provided conclusion is false or partially false and identifies which component is wrong. Give 0.5 points if the response provides the corrected answer, meaning the true dominant band and true sleep stage, or correctly fixes each wrong component while preserving any component that was already correct. If the response agrees with the provided false conclusion or says the conclusion is valid without finding any error, the score must be 0.0. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only one valid JSON object: {\"score\": 0.0}, {\"score\": 0.5}, or {\"score\": 1.0}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_01.json b/sleep_assessment/cases/case26/case26_01.json deleted file mode 100644 index a67b56f0228556607b0343cbb4262afb834f1129..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_01.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 274.5 to 275.5, B = minute 63.0 to 64.0, C = minute 437.0 to 438.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_02.json b/sleep_assessment/cases/case26/case26_02.json deleted file mode 100644 index 84b302f5d87920aa259eafb50eb41baa153bc76f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_02.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 473.5 to 474.5, B = minute 359.5 to 360.5, C = minute 135.5 to 136.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_03.json b/sleep_assessment/cases/case26/case26_03.json deleted file mode 100644 index a6349a2e307a7f5bcebbca0f5fe78a0e735154b0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_03.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 210.5 to 211.5, B = minute 334.0 to 335.0, C = minute 322.0 to 323.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_04.json b/sleep_assessment/cases/case26/case26_04.json deleted file mode 100644 index accb13895fb27c4f4e1cb6cb75e14c079c63b1fc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_04.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 349.0 to 350.0, B = minute 375.0 to 376.0, C = minute 97.0 to 98.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_05.json b/sleep_assessment/cases/case26/case26_05.json deleted file mode 100644 index 155aeee9fb7fdb5e067995ecaa18a1edd8cc359a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_05.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 64.0 to 65.0, B = minute 423.0 to 424.0, C = minute 366.5 to 367.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_06.json b/sleep_assessment/cases/case26/case26_06.json deleted file mode 100644 index 0bb0228b82c024f6ecb6736418279b7d1c687d2d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_06.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 278.5 to 279.5, B = minute 24.5 to 25.5, C = minute 398.5 to 399.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_07.json b/sleep_assessment/cases/case26/case26_07.json deleted file mode 100644 index 274b21fb17676bd0904993eba807fb14a5f8344e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_07.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 409.0 to 410.0, B = minute 217.5 to 218.5, C = minute 60.5 to 61.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_08.json b/sleep_assessment/cases/case26/case26_08.json deleted file mode 100644 index 7e6579364e87104aea515dd50233042b52016582..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_08.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 110.0 to 111.0, B = minute 461.5 to 462.5, C = minute 65.5 to 66.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_09.json b/sleep_assessment/cases/case26/case26_09.json deleted file mode 100644 index 204edc575f3da3de65d3eec05f2eb773bce6e52d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_09.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 353.5 to 354.5, B = minute 387.5 to 388.5, C = minute 5.0 to 6.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_10.json b/sleep_assessment/cases/case26/case26_10.json deleted file mode 100644 index 3e707a5dfe4b343eea4ad6023bd00b13968332c3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_10.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 427.0 to 428.0, B = minute 19.5 to 20.5, C = minute 244.5 to 245.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_11.json b/sleep_assessment/cases/case26/case26_11.json deleted file mode 100644 index 1414372578f27ca86f4320986e9d1f50091df3c2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_11.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 432.5 to 433.5, B = minute 96.5 to 97.5, C = minute 168.5 to 169.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_12.json b/sleep_assessment/cases/case26/case26_12.json deleted file mode 100644 index 192d26bafae1de1e9613a72ec2c31b0f51f31ec8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_12.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 223.0 to 224.0, B = minute 423.5 to 424.5, C = minute 230.0 to 231.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_13.json b/sleep_assessment/cases/case26/case26_13.json deleted file mode 100644 index 7269b23f4b67e8456a752e54421b22d5b93446ef..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_13.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 144.0 to 145.0, B = minute 439.5 to 440.5, C = minute 461.0 to 462.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_14.json b/sleep_assessment/cases/case26/case26_14.json deleted file mode 100644 index f332a8c060e1f0fddf3c4152da5b281bbc65d819..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_14.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 436.5 to 437.5, B = minute 305.0 to 306.0, C = minute 124.5 to 125.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_15.json b/sleep_assessment/cases/case26/case26_15.json deleted file mode 100644 index 324e9dffd19913a87055faaba42152cb4532933f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_15.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 472.0 to 473.0, B = minute 316.5 to 317.5, C = minute 12.0 to 13.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_16.json b/sleep_assessment/cases/case26/case26_16.json deleted file mode 100644 index 0b52b2e3206e1928103806dd432b0c24b57f876a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_16.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 371.0 to 372.0, B = minute 219.0 to 220.0, C = minute 425.5 to 426.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_17.json b/sleep_assessment/cases/case26/case26_17.json deleted file mode 100644 index 283c0395050a0aad873cd464907f1883960fe83f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_17.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 375.5 to 376.5, B = minute 463.5 to 464.5, C = minute 16.5 to 17.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_18.json b/sleep_assessment/cases/case26/case26_18.json deleted file mode 100644 index 26070db752d9558f9504598a12c7fe55c12cbe8e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_18.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 54.5 to 55.5, B = minute 193.0 to 194.0, C = minute 410.0 to 411.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_19.json b/sleep_assessment/cases/case26/case26_19.json deleted file mode 100644 index fae1bb20523a9cba4e83529146397d9a926b28ea..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_19.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 0.0 to 1.0, B = minute 176.5 to 177.5, C = minute 133.5 to 134.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_20.json b/sleep_assessment/cases/case26/case26_20.json deleted file mode 100644 index 67dd49b0c7136279af41cd7bd502a5bea6c31e6e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_20.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 356.0 to 357.0, B = minute 407.0 to 408.0, C = minute 300.5 to 301.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_21.json b/sleep_assessment/cases/case26/case26_21.json deleted file mode 100644 index ba233c29aad63449fc879aeeb9d661084c625d32..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_21.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 227.5 to 228.5, B = minute 196.0 to 197.0, C = minute 96.0 to 97.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_22.json b/sleep_assessment/cases/case26/case26_22.json deleted file mode 100644 index eff6b582b753f2da1c96565ee8c3db7ae2db43d8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_22.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 291.0 to 292.0, B = minute 232.5 to 233.5, C = minute 168.5 to 169.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_23.json b/sleep_assessment/cases/case26/case26_23.json deleted file mode 100644 index 34c0cd06662de5a422807440bb53c4b4a43c030c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_23.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 102.0 to 103.0, B = minute 425.0 to 426.0, C = minute 475.0 to 476.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_24.json b/sleep_assessment/cases/case26/case26_24.json deleted file mode 100644 index 91a0bd788369ec74e0ce20093b7d15b6c32fad94..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_24.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 62.0 to 63.0, B = minute 268.0 to 269.0, C = minute 313.0 to 314.0. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case26/case26_25.json b/sleep_assessment/cases/case26/case26_25.json deleted file mode 100644 index 155525832cd604753c7da8b49ca8b60f84b831ff..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case26/case26_25.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 389.5 to 390.5, B = minute 381.0 to 382.0, C = minute 206.5 to 207.5. Compare the EOG/eye-movement activity strength across A, B, and C. Report the segment labels in descending order of EOG activity strength, and state whether the strongest segment is REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two final outputs from the agent's report:\n1) the descending order of EOG activity strength across segments A, B, and C\n2) whether the strongest segment is REM sleep\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"eog_activity_order_desc\" and \"strongest_segment_is_rem\".\n5. \"eog_activity_order_desc\" must be a JSON array with EXACTLY 3 elements.\n6. The array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. \"strongest_segment_is_rem\" must be exactly one of: \"yes\", \"no\", or null.\n8. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n9. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"eog_activity_order_desc\": [\"B\", \"A\", \"C\"], \"strongest_segment_is_rem\": \"yes\"}", - "metrics": [ - { - "metric_id": "eog_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "eog_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "strongest_segment_is_rem_accuracy", - "type": "categorical_check", - "target_key": "strongest_segment_is_rem", - "weight": 40, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_01.json b/sleep_assessment/cases/case27/case27_01.json deleted file mode 100644 index 911060fa418b541ad0729d6cc125fdf3cef45b57..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_01.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 274.5 to 275.5, B = minute 4.0 to 5.0, C = minute 194.5 to 195.5. For this file, X1, X3, and X4 are EMG channels. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is C. The response is correct only if it states that REM sleep is present and identifies segment C as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_02.json b/sleep_assessment/cases/case27/case27_02.json deleted file mode 100644 index ef74b4b0998ece05ff27d243c950f79f5801e6d5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_02.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 239.0 to 240.0, B = minute 42.0 to 43.0, C = minute 36.0 to 37.0. For this file, X1, X3, and X4 are EMG channels. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is A. The response is correct only if it states that REM sleep is present and identifies segment A as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_03.json b/sleep_assessment/cases/case27/case27_03.json deleted file mode 100644 index ddc38ca32f9be9cfe639440cc1e464fd92a132c0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_03.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 226.0 to 227.0, B = minute 169.5 to 170.5, C = minute 222.5 to 223.5. For this file, X1, X3, and X4 are EMG channels. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is B. The response is correct only if it states that REM sleep is present and identifies segment B as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_04.json b/sleep_assessment/cases/case27/case27_04.json deleted file mode 100644 index 69b60b90f9acd3490522cd9d6041853485453f60..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_04.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 258.0 to 259.0, B = minute 155.5 to 156.5, C = minute 3.0 to 4.0. For this file, X1, X3, and X4 are EMG channels. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_05.json b/sleep_assessment/cases/case27/case27_05.json deleted file mode 100644 index 35f0c0d8762d6f8a50405b430df086635bb4b972..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_05.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 31.0 to 32.0, B = minute 28.0 to 29.0, C = minute 200.5 to 201.5. For this file, X1, X3, and X4 are EMG channels. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_06.json b/sleep_assessment/cases/case27/case27_06.json deleted file mode 100644 index cbde80d1613bb06212a6b36e324e7a5ebff8769c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_06.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 189.5 to 190.5, B = minute 392.5 to 393.5, C = minute 73.0 to 74.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_07.json b/sleep_assessment/cases/case27/case27_07.json deleted file mode 100644 index a077b0b0196ba87db8985650f3c659bed6d7e371..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_07.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 193.0 to 194.0, B = minute 91.5 to 92.5, C = minute 4.5 to 5.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_08.json b/sleep_assessment/cases/case27/case27_08.json deleted file mode 100644 index 8010e0ab0d0ff10389a89bf7df18561a9586a8c2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_08.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 359.0 to 360.0, B = minute 294.0 to 295.0, C = minute 180.5 to 181.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is B. The response is correct only if it states that REM sleep is present and identifies segment B as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_09.json b/sleep_assessment/cases/case27/case27_09.json deleted file mode 100644 index 1a9c0fb23c5d9f0e7f741f50333bce454b748472..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_09.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 0.0 to 1.0, B = minute 454.0 to 455.0, C = minute 7.5 to 8.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is B. The response is correct only if it states that REM sleep is present and identifies segment B as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_10.json b/sleep_assessment/cases/case27/case27_10.json deleted file mode 100644 index 3a785bcbe0680be53191e8aefef69d54b6f224f3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_10.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 332.5 to 333.5, B = minute 58.0 to 59.0, C = minute 396.0 to 397.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is C. The response is correct only if it states that REM sleep is present and identifies segment C as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_11.json b/sleep_assessment/cases/case27/case27_11.json deleted file mode 100644 index f566e85454761ff60fcb74db0e76a1f06a037f90..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_11.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 529.5 to 530.5, B = minute 172.0 to 173.0, C = minute 239.0 to 240.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is C. The response is correct only if it states that REM sleep is present and identifies segment C as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_12.json b/sleep_assessment/cases/case27/case27_12.json deleted file mode 100644 index 7cc7bdf56126a47e49b2e7daf1a07bb0d3705faa..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_12.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 537.5 to 538.5, B = minute 226.0 to 227.0, C = minute 161.0 to 162.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_13.json b/sleep_assessment/cases/case27/case27_13.json deleted file mode 100644 index d46aa9ac14d499f4b581deb28a259b19ffdba7b2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_13.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 430.0 to 431.0, B = minute 445.0 to 446.0, C = minute 48.0 to 49.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_14.json b/sleep_assessment/cases/case27/case27_14.json deleted file mode 100644 index 75d21d99cf5b8084391314b9965e41fd8614ba09..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_14.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 30.5 to 31.5, B = minute 153.5 to 154.5, C = minute 317.5 to 318.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is B. The response is correct only if it states that REM sleep is present and identifies segment B as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_15.json b/sleep_assessment/cases/case27/case27_15.json deleted file mode 100644 index 27b33bcd9d5e284035c832b7c837b8b6dc58d268..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_15.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 353.5 to 354.5, B = minute 1.5 to 2.5, C = minute 306.5 to 307.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is C. The response is correct only if it states that REM sleep is present and identifies segment C as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_16.json b/sleep_assessment/cases/case27/case27_16.json deleted file mode 100644 index cee5d9ba0bc89eb677b375c5fdc100b0e937b110..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_16.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 415.5 to 416.5, B = minute 450.0 to 451.0, C = minute 291.0 to 292.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_17.json b/sleep_assessment/cases/case27/case27_17.json deleted file mode 100644 index 543073ac98bd139193e15254f227ced2092cead5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_17.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 477.0 to 478.0, B = minute 407.0 to 408.0, C = minute 0.5 to 1.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is A. The response is correct only if it states that REM sleep is present and identifies segment A as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_18.json b/sleep_assessment/cases/case27/case27_18.json deleted file mode 100644 index 841e770c9f1e9c9d111a7befacae9f4c6c9efca3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_18.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 408.0 to 409.0, B = minute 365.0 to 366.0, C = minute 0.0 to 1.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is B. The response is correct only if it states that REM sleep is present and identifies segment B as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_19.json b/sleep_assessment/cases/case27/case27_19.json deleted file mode 100644 index 585cabbf1e17955e0ee26a322b3b060fc3cb0ac4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_19.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 42.0 to 43.0, B = minute 406.5 to 407.5, C = minute 18.0 to 19.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_20.json b/sleep_assessment/cases/case27/case27_20.json deleted file mode 100644 index b0ad4b30eb6fa385b39eb294f5cf3978c09c8b5f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_20.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 205.5 to 206.5, B = minute 392.0 to 393.0, C = minute 187.5 to 188.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is C. The response is correct only if it states that REM sleep is present and identifies segment C as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_21.json b/sleep_assessment/cases/case27/case27_21.json deleted file mode 100644 index 67619536540c9feaa9c57e2339c008cd06f98955..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_21.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 0.0 to 1.0, B = minute 73.0 to 74.0, C = minute 237.5 to 238.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "B", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is C. The response is correct only if it states that REM sleep is present and identifies segment C as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_22.json b/sleep_assessment/cases/case27/case27_22.json deleted file mode 100644 index 5f6958c6781589f422080e1ebb1bfaa4f659c1fd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_22.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 201.0 to 202.0, B = minute 212.5 to 213.5, C = minute 371.0 to 372.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "C", - "A" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_23.json b/sleep_assessment/cases/case27/case27_23.json deleted file mode 100644 index 2ee0d78f4e0b03788720650fbdc334ad666c88f1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_23.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 448.0 to 449.0, B = minute 364.5 to 365.5, C = minute 137.0 to 138.0. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "C", - "A", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is not present among A/B/C. The response is correct only if it states that REM sleep is absent or that none of the three segments is REM. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_24.json b/sleep_assessment/cases/case27/case27_24.json deleted file mode 100644 index a0e07e227fd7796cbfd49701fe2873862936895b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_24.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 309.0 to 310.0, B = minute 70.5 to 71.5, C = minute 293.5 to 294.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "A", - "C", - "B" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is B. The response is correct only if it states that REM sleep is present and identifies segment B as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case27/case27_25.json b/sleep_assessment/cases/case27/case27_25.json deleted file mode 100644 index 002484fde57f6b3bac8800347dd64de481200764..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case27/case27_25.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please analyze the provided sleep file and examine the following three 1-minute sleep segments: A = minute 296.5 to 297.5, B = minute 6.0 to 7.0, C = minute 397.5 to 398.5. Compare the EMG activity strength across A, B, and C. Report the segment labels in descending order of EMG activity strength, and state whether any of the three segments is REM sleep. If REM sleep is present, identify which segment it is." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EMG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the descending EMG activity order across A/B/C and preserve the full report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text outside the JSON object.\n4. The keys must be EXACTLY \"emg_activity_order_desc\" and \"full_text\".\n5. \"emg_activity_order_desc\" must be a JSON array with EXACTLY 3 elements or null.\n6. If present, the array elements must be exactly \"A\", \"B\", and \"C\", without duplicates, in descending order.\n7. If the report gives an order like A>B>C or A, B, C, convert it to the JSON array form.\n8. \"full_text\" must be the complete original report text as a string. If no report text is available, use null.\n\n### OUTPUT TEMPLATE\n{\"emg_activity_order_desc\": [\"B\", \"A\", \"C\"], \"full_text\": }", - "metrics": [ - { - "metric_id": "emg_activity_order_accuracy", - "type": "sequence_match_check", - "target_key": "emg_activity_order_desc", - "weight": 60, - "params": { - "gt_value": [ - "B", - "A", - "C" - ], - "match_mode": "exact_order" - } - }, - { - "metric_id": "rem_presence_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. REM sleep is present among A/B/C, and the correct segment is C. The response is correct only if it states that REM sleep is present and identifies segment C as the REM segment. Mark the response incorrect if it says REM is present when it is absent, says REM is absent when it is present, identifies the wrong segment, identifies multiple segments, or gives an ambiguous conclusion about REM presence. Output only one valid JSON object: {\"status\": true} or {\"status\": false}. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_01.json b/sleep_assessment/cases/case28/case28_01.json deleted file mode 100644 index 4f9900e0bf8b98d734a4d2c4595234874357a747..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_01.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. For this ISRUC file, X2 is the ECG channel. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 60.65368360694034, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 51.06382978723404, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 77.6707163803938, - "tolerance": 7.76707163803938 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_02.json b/sleep_assessment/cases/case28/case28_02.json deleted file mode 100644 index 68dab933e7285c6a3bc9a04bc645f91b8ce44b17..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_02.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. For this ISRUC file, X2 is the ECG channel. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 64.83364781619402, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 57.69230769230769, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 76.43312101910827, - "tolerance": 7.643312101910827 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_03.json b/sleep_assessment/cases/case28/case28_03.json deleted file mode 100644 index aee2b7e77f54d259787b4e0e1e06e9bb30138a55..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_03.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. For this ISRUC file, X2 is the ECG channel. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 64.37798526448601, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 51.724137931034484, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 85.1063829787234, - "tolerance": 8.51063829787234 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_04.json b/sleep_assessment/cases/case28/case28_04.json deleted file mode 100644 index 2c918cdeac67f1af46614d9897610b71bd162a50..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_04.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. For this ISRUC file, X2 is the ECG channel. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 49.89002583050498, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 39.34426229508197, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 76.43312101910827, - "tolerance": 7.643312101910827 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_05.json b/sleep_assessment/cases/case28/case28_05.json deleted file mode 100644 index 88acf40c575c2e26edd910494f48ebcacc9cd17d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_05.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. For this ISRUC file, X2 is the ECG channel. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 50.72243613035606, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 44.11764705882353, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 81.08478290645117, - "tolerance": 8.108478290645117 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_06.json b/sleep_assessment/cases/case28/case28_06.json deleted file mode 100644 index 628a8fd7c71dd5b727316eb5d7e97570d935460d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_06.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_01.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 88.42568986196196, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 75.29411764705883, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 131.28205128205127, - "tolerance": 13.128205128205128 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_07.json b/sleep_assessment/cases/case28/case28_07.json deleted file mode 100644 index 831940c217edb70bc876384a1402cee4908c62f0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_07.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_02.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 46.807686841605936, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 41.6260162601626, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 76.41791044776119, - "tolerance": 7.641791044776119 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_08.json b/sleep_assessment/cases/case28/case28_08.json deleted file mode 100644 index c4656242d8387419a60e00db13fe896b3a464324..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_08.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_03.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 58.02496343340899, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 50.19607843137255, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 100.3921568627451, - "tolerance": 10.03921568627451 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_09.json b/sleep_assessment/cases/case28/case28_09.json deleted file mode 100644 index f67609e56dde2d94e369a25b4a77bab53f88e2ea..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_09.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_04.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 57.397448859366236, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 49.38906752411575, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 82.3598412972227, - "tolerance": 8.23598412972227 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_10.json b/sleep_assessment/cases/case28/case28_10.json deleted file mode 100644 index bc7b70318e9bc7753c06dc801cfa7ccb2e7d8877..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_10.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "data_path": "data/sleep/HMC_05.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 62.33478589741015, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 54.661921708185055, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 101.72185430463576, - "tolerance": 10.172185430463577 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_11.json b/sleep_assessment/cases/case28/case28_11.json deleted file mode 100644 index 06aba6d79d3708925b694d34656e42774dd4baff..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_11.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 70.00431226388605, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 59.28946381702288, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 118.11755952380952, - "tolerance": 11.811755952380953 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_12.json b/sleep_assessment/cases/case28/case28_12.json deleted file mode 100644 index 5ab526f14b9e76090b6504e839b4cfbcbb783495..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_12.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 106.28390622107275, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 50.33557046979866, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 192.30769230769232, - "tolerance": 19.230769230769234 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_13.json b/sleep_assessment/cases/case28/case28_13.json deleted file mode 100644 index d73d56fa557edefa31404f4c4a5903cbf86acd94..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_13.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 71.97590376954118, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 60.97560975609756, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 115.38461538461539, - "tolerance": 11.53846153846154 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_14.json b/sleep_assessment/cases/case28/case28_14.json deleted file mode 100644 index 2bd95b5060dfd10f1b52e864fe7d8f4c6ab7db84..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_14.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 60.66056818445853, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 52.81690140845071, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 111.11720807726076, - "tolerance": 11.111720807726076 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_15.json b/sleep_assessment/cases/case28/case28_15.json deleted file mode 100644 index 864942296cd9a5c33741a7d12f2be51e48643296..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_15.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 78.74478672433858, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 67.56756756756756, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 113.63636363636363, - "tolerance": 11.363636363636363 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_16.json b/sleep_assessment/cases/case28/case28_16.json deleted file mode 100644 index 8fe29207f2a9dc11d6202e044bbf9cd99faf6ae3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_16.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_01.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 58.403546855412756, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 50.114325860637415, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 74.02452619843925, - "tolerance": 7.402452619843925 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_17.json b/sleep_assessment/cases/case28/case28_17.json deleted file mode 100644 index ef3d709bf7871c69127f5e3975debf3d60d453c5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_17.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_02.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 54.36498724436511, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 45.714285714285715, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 72.11267605633803, - "tolerance": 7.211267605633804 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_18.json b/sleep_assessment/cases/case28/case28_18.json deleted file mode 100644 index ad3f39d696b26c0ca58554d03df99302d0993bd3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_18.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_03.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 68.07917612862003, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 60.23529411764706, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 90.3529411764706, - "tolerance": 9.03529411764706 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_19.json b/sleep_assessment/cases/case28/case28_19.json deleted file mode 100644 index 6aef3b5a5f1cf1cdb7a429a8f61b5dca321d524b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_19.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_04.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 51.09511192507425, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 45.309734513274336, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 66.49350649350649, - "tolerance": 6.649350649350649 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_20.json b/sleep_assessment/cases/case28/case28_20.json deleted file mode 100644 index a554050e381a4632e0e9cb6d2d06093e7e475b39..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_20.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "data_path": "data/sleep/MASSSS3_05.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 61.374637268140745, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 53.5191637630662, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 84.86446886446886, - "tolerance": 8.486446886446886 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_21.json b/sleep_assessment/cases/case28/case28_21.json deleted file mode 100644 index b43c2d58a0d53789312b4314b750de2756fcbe5d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_21.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 63.89444633974605, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 56.60377358490566, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 77.1712158808933, - "tolerance": 7.717121588089331 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_22.json b/sleep_assessment/cases/case28/case28_22.json deleted file mode 100644 index 987cae5c40b40ca0e386f6f9e036b2e600f00b35..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_22.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 46.92386963415311, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 37.73584905660377, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 72.29178007621121, - "tolerance": 7.229178007621122 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_23.json b/sleep_assessment/cases/case28/case28_23.json deleted file mode 100644 index 9aac6840b6bdc79777d9a08e024da22628dea552..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_23.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 77.1206828253841, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 71.00591715976331, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 90.9090909090909, - "tolerance": 9.090909090909092 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_24.json b/sleep_assessment/cases/case28/case28_24.json deleted file mode 100644 index 0860369894b806d091bc8b599ecc5a124aeda884..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_24.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 97.19501874017652, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 89.55223880597015, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 110.09174311926604, - "tolerance": 11.009174311926605 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case28/case28_25.json b/sleep_assessment/cases/case28/case28_25.json deleted file mode 100644 index 957377e6acf10996a0499144b0bce02fd8d5b501..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case28/case28_25.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Please use the provided sleep file to identify the ECG channel and estimate whole-night heart-rate statistics from the ECG signal. Report three final values: mean_HR_bpm, min_HR_bpm, and max_HR_bpm." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep ECG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three final whole-night heart-rate statistics from the agent's report:\n1) mean_HR_bpm\n2) min_HR_bpm\n3) max_HR_bpm\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"mean_HR_bpm\", \"min_HR_bpm\", and \"max_HR_bpm\".\n5. All three values must be FLOAT bpm values or null. Do not include units.\n6. If the report contains units like bpm or beats/min, strip the units and keep only the number.\n7. If one value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"mean_HR_bpm\": , \"min_HR_bpm\": , \"max_HR_bpm\": }", - "metrics": [ - { - "metric_id": "mean_hr_accuracy", - "type": "numeric_check", - "target_key": "mean_HR_bpm", - "weight": 34, - "params": { - "gt_value": 75.95668174580578, - "tolerance": 3.0 - } - }, - { - "metric_id": "min_hr_accuracy", - "type": "numeric_check", - "target_key": "min_HR_bpm", - "weight": 33, - "params": { - "gt_value": 67.0391061452514, - "tolerance": 5.0 - } - }, - { - "metric_id": "max_hr_accuracy", - "type": "numeric_check", - "target_key": "max_HR_bpm", - "weight": 33, - "params": { - "gt_value": 95.23809523809524, - "tolerance": 9.523809523809524 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_01.json b/sleep_assessment/cases/case29/case29_01.json deleted file mode 100644 index d9c063ae1dc90f63d85dbc41200154606f9c6029..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_01.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_01_ISRUC_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 28.0 to 29.0, C = minute 66.0 to 67.0, D = minute 94.0 to 95.0, E = minute 123.0 to 124.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_02.json b/sleep_assessment/cases/case29/case29_02.json deleted file mode 100644 index 6467aee4f0841200d5809452615204edf2b6d729..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_02.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_02_ISRUC_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 151.0 to 152.0, B = minute 188.0 to 189.0, C = minute 33.25 to 34.25, D = minute 234.0 to 235.0, E = minute 281.0 to 282.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_03.json b/sleep_assessment/cases/case29/case29_03.json deleted file mode 100644 index e8f4487935e47fbf14b3c74cbc4ad579dbfa9a9b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_03.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_03_ISRUC_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 177.75 to 178.75, B = minute 309.0 to 310.0, C = minute 349.0 to 350.0, D = minute 377.0 to 378.0, E = minute 226.75 to 227.75. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_04.json b/sleep_assessment/cases/case29/case29_04.json deleted file mode 100644 index e345703e937ef1151b4c903757acbcd4ae4aa188..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_04.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_04_ISRUC_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 406.0 to 407.0, B = minute 246.75 to 247.75, C = minute 322.25 to 323.25, D = minute 438.75 to 439.75, E = minute 438.0 to 439.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_05.json b/sleep_assessment/cases/case29/case29_05.json deleted file mode 100644 index e60002e2e03b1edbc6ca136e1c0f9965e4b1a6bc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_05.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_05_ISRUC_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 31.0 to 32.0, C = minute 63.0 to 64.0, D = minute 106.0 to 107.0, E = minute 159.0 to 160.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_06.json b/sleep_assessment/cases/case29/case29_06.json deleted file mode 100644 index c3fbb88f716766df8c6b1898cbe4e419d14c59cf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_06.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_06_ISRUC_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 194.0 to 195.0, B = minute 230.0 to 231.0, C = minute 87.25 to 88.25, D = minute 262.0 to 263.0, E = minute 295.0 to 296.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_07.json b/sleep_assessment/cases/case29/case29_07.json deleted file mode 100644 index b89795cc836a2c64250b2086c9c23e52626658a8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_07.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_07_ISRUC_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 123.75 to 124.75, B = minute 328.0 to 329.0, C = minute 369.0 to 370.0, D = minute 408.0 to 409.0, E = minute 147.75 to 148.75. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_08.json b/sleep_assessment/cases/case29/case29_08.json deleted file mode 100644 index e533eb3fdb3c8f48e945c0d3fd32477e799d46f5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_08.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_08_ISRUC_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 442.0 to 443.0, B = minute 319.25 to 320.25, C = minute 379.75 to 380.75, D = minute 481.0 to 482.0, E = minute 479.0 to 480.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_09.json b/sleep_assessment/cases/case29/case29_09.json deleted file mode 100644 index 96e7d1438d38e8f6f5ef99714f6a91d6d3a1c3d7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_09.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_09_ISRUC_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 45.0 to 46.0, C = minute 82.0 to 83.0, D = minute 119.0 to 120.0, E = minute 149.0 to 150.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_10.json b/sleep_assessment/cases/case29/case29_10.json deleted file mode 100644 index 54e31b5e5b7beff6abf29c18452390f9d51aba12..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_10.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_10_ISRUC_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 187.0 to 188.0, B = minute 219.0 to 220.0, C = minute 5.25 to 6.25, D = minute 253.0 to 254.0, E = minute 287.0 to 288.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_11.json b/sleep_assessment/cases/case29/case29_11.json deleted file mode 100644 index 955c88eb582b2e72833faa003da1cce80fc910fe..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_11.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_11_ISRUC_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 62.75 to 63.75, B = minute 326.0 to 327.0, C = minute 370.0 to 371.0, D = minute 405.0 to 406.0, E = minute 170.75 to 171.75. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_12.json b/sleep_assessment/cases/case29/case29_12.json deleted file mode 100644 index 2497e2c68a8ead43364972b725e71605922b62d1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_12.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_12_ISRUC_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 435.0 to 436.0, B = minute 273.75 to 274.75, C = minute 345.25 to 346.25, D = minute 470.5 to 471.5, E = minute 469.0 to 470.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_13.json b/sleep_assessment/cases/case29/case29_13.json deleted file mode 100644 index f5f69dfbeee62a0474b4e3a5abf042fd66da77af..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_13.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_13_ISRUC_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 40.0 to 41.0, C = minute 77.0 to 78.0, D = minute 113.0 to 114.0, E = minute 149.0 to 150.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_14.json b/sleep_assessment/cases/case29/case29_14.json deleted file mode 100644 index 7e6f0488bfbe9892c2a373f559324de82e326a70..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_14.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_14_ISRUC_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 187.0 to 188.0, B = minute 224.0 to 225.0, C = minute 2.75 to 3.75, D = minute 260.0 to 261.0, E = minute 297.0 to 298.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_15.json b/sleep_assessment/cases/case29/case29_15.json deleted file mode 100644 index e27ea2f10b44e3c31f37fd54f59744576c2ab6d5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_15.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_15_ISRUC_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 16.25 to 17.25, B = minute 334.0 to 335.0, C = minute 372.0 to 373.0, D = minute 408.0 to 409.0, E = minute 45.25 to 46.25. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_16.json b/sleep_assessment/cases/case29/case29_16.json deleted file mode 100644 index 6eb7729acdd3fa39aff764c4d0f3d4e66744049d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_16.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_16_ISRUC_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 444.0 to 445.0, B = minute 199.25 to 200.25, C = minute 298.25 to 299.25, D = minute 345.75 to 346.75, E = minute 480.0 to 481.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_17.json b/sleep_assessment/cases/case29/case29_17.json deleted file mode 100644 index 1c94298eb241a3ca1975492326f9d830e78b4eef..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_17.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_17_ISRUC_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 29.0 to 30.0, C = minute 58.0 to 59.0, D = minute 92.0 to 93.0, E = minute 122.0 to 123.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_18.json b/sleep_assessment/cases/case29/case29_18.json deleted file mode 100644 index eddda02cfcf77d113d61308392043c37f863c575..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_18.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_18_ISRUC_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 156.0 to 157.0, B = minute 185.0 to 186.0, C = minute 75.25 to 76.25, D = minute 218.0 to 219.0, E = minute 252.0 to 253.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_19.json b/sleep_assessment/cases/case29/case29_19.json deleted file mode 100644 index 9b09d7f6df17ee9b5b9191b14e409e03ea0167c9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_19.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_19_ISRUC_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 206.75 to 207.75, B = minute 286.0 to 287.0, C = minute 336.0 to 337.0, D = minute 377.0 to 378.0, E = minute 268.75 to 269.75. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_20.json b/sleep_assessment/cases/case29/case29_20.json deleted file mode 100644 index 361fb9716d54ec917e7b32f4ed3d8f77ea0ea486..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_20.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_20_ISRUC_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 407.0 to 408.0, B = minute 308.75 to 309.75, C = minute 342.25 to 343.25, D = minute 403.25 to 404.25, E = minute 436.0 to 437.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_21.json b/sleep_assessment/cases/case29/case29_21.json deleted file mode 100644 index 0d99e278c0da20667b5fe0c4f3519f3bff162227..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_21.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_21_SHHS1_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 35.0 to 36.0, C = minute 97.0 to 98.0, D = minute 157.0 to 158.0, E = minute 188.0 to 189.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_22.json b/sleep_assessment/cases/case29/case29_22.json deleted file mode 100644 index 5d8152b86370f18419fc6aa507ad46399681ebd7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_22.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_22_SHHS1_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 216.0 to 217.0, B = minute 259.0 to 260.0, C = minute 15.79 to 16.79, D = minute 291.0 to 292.0, E = minute 343.0 to 344.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_23.json b/sleep_assessment/cases/case29/case29_23.json deleted file mode 100644 index 2139f25b502bc9fb9eff97f1fe2311a2513c3ef7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_23.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_23_SHHS1_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 88.3 to 89.3, B = minute 388.0 to 389.0, C = minute 456.0 to 457.0, D = minute 484.0 to 485.0, E = minute 141.58 to 142.58. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_24.json b/sleep_assessment/cases/case29/case29_24.json deleted file mode 100644 index e7b6cfb8603d1fe77c2114f472869a35e02718e3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_24.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_24_SHHS1_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 513.0 to 514.0, B = minute 313.53 to 314.53, C = minute 400.86 to 401.86, D = minute 454.28 to 455.28, E = minute 541.0 to 542.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_25.json b/sleep_assessment/cases/case29/case29_25.json deleted file mode 100644 index f789c8ad0892a0639f64ef3a3c6e1d6d7a19a02b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_25.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_25_SHHS1_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 41.0 to 42.0, C = minute 102.0 to 103.0, D = minute 147.0 to 148.0, E = minute 207.0 to 208.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_26.json b/sleep_assessment/cases/case29/case29_26.json deleted file mode 100644 index 06cf43db1d36e38a0d36110dafe4daa9ca9517d5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_26.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_26_SHHS1_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 257.0 to 258.0, B = minute 292.0 to 293.0, C = minute 3.44 to 4.44, D = minute 328.0 to 329.0, E = minute 363.0 to 364.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_27.json b/sleep_assessment/cases/case29/case29_27.json deleted file mode 100644 index 1674b4abdd06e555badd2d369db236d0c8d5899a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_27.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_27_SHHS1_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 70.53 to 71.53, B = minute 398.0 to 399.0, C = minute 433.0 to 434.0, D = minute 468.0 to 469.0, E = minute 123.24 to 124.24. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_28.json b/sleep_assessment/cases/case29/case29_28.json deleted file mode 100644 index 7212951e29ba3c6317cfafb8dda7ddf1733441fb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_28.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_28_SHHS1_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 503.0 to 504.0, B = minute 170.3 to 171.3, C = minute 200.78 to 201.78, D = minute 224.79 to 225.79, E = minute 538.0 to 539.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_29.json b/sleep_assessment/cases/case29/case29_29.json deleted file mode 100644 index 411bc515088c7402a274a078ed6900653694c4e4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_29.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_29_SHHS1_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 30.0 to 31.0, C = minute 60.0 to 61.0, D = minute 92.0 to 93.0, E = minute 137.0 to 138.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_30.json b/sleep_assessment/cases/case29/case29_30.json deleted file mode 100644 index b1b070cf3d6037665f4886570ae7d49d06d5e2de..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_30.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_30_SHHS1_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 179.0 to 180.0, B = minute 239.0 to 240.0, C = minute 83.56 to 84.56, D = minute 281.0 to 282.0, E = minute 324.0 to 325.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_31.json b/sleep_assessment/cases/case29/case29_31.json deleted file mode 100644 index 2bd18184e879bf8a30c492add601cfe614f250ba..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_31.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_31_SHHS1_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 168.06 to 169.06, B = minute 355.0 to 356.0, C = minute 393.0 to 394.0, D = minute 453.0 to 454.0, E = minute 226.73 to 227.73. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_32.json b/sleep_assessment/cases/case29/case29_32.json deleted file mode 100644 index bc1853c06fade2868ba3aa0e5c5a077a62743b0e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_32.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_32_SHHS1_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 488.0 to 489.0, B = minute 302.65 to 303.65, C = minute 413.82 to 414.82, D = minute 514.6 to 515.6, E = minute 523.0 to 524.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_33.json b/sleep_assessment/cases/case29/case29_33.json deleted file mode 100644 index 458762bc25337ca3d6a487de12356c2742f7b020..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_33.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_33_SHHS1_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 30.0 to 31.0, C = minute 63.0 to 64.0, D = minute 94.0 to 95.0, E = minute 126.0 to 127.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_34.json b/sleep_assessment/cases/case29/case29_34.json deleted file mode 100644 index 0f90d538579830338df7f7c689b5669afddb8587..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_34.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_34_SHHS1_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 158.0 to 159.0, B = minute 193.0 to 194.0, C = minute 42.13 to 43.13, D = minute 224.0 to 225.0, E = minute 259.0 to 260.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_35.json b/sleep_assessment/cases/case29/case29_35.json deleted file mode 100644 index 4a92cbd2e50ec9b90d20f7e3e8b19944f8d2a3a5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_35.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_35_SHHS1_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 172.91 to 173.91, B = minute 295.0 to 296.0, C = minute 335.0 to 336.0, D = minute 365.0 to 366.0, E = minute 249.01 to 250.01. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_36.json b/sleep_assessment/cases/case29/case29_36.json deleted file mode 100644 index f54cddc180454f34e2e2b5b9e63f48e1c4256b97..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_36.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_36_SHHS1_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 406.0 to 407.0, B = minute 297.65 to 298.65, C = minute 365.86 to 366.86, D = minute 385.14 to 386.14, E = minute 436.0 to 437.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_37.json b/sleep_assessment/cases/case29/case29_37.json deleted file mode 100644 index 011a4a9a28b6a884c67bf5c26029a84ebe5f0eb6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_37.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_37_SHHS1_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 0.0 to 1.0, B = minute 33.0 to 34.0, C = minute 73.0 to 74.0, D = minute 108.0 to 109.0, E = minute 142.0 to 143.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_38.json b/sleep_assessment/cases/case29/case29_38.json deleted file mode 100644 index 6bb0c0456254539693e3a27d9c0b63fdd4186b86..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_38.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_38_SHHS1_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 194.0 to 195.0, B = minute 236.0 to 237.0, C = minute 10.81 to 11.81, D = minute 283.0 to 284.0, E = minute 336.0 to 337.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_39.json b/sleep_assessment/cases/case29/case29_39.json deleted file mode 100644 index d5a462e9b13172c7c6c06a9cc382d1ae6930bd15..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_39.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_39_SHHS1_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 159.44 to 160.44, B = minute 369.0 to 370.0, C = minute 416.0 to 417.0, D = minute 469.0 to 470.0, E = minute 251.2 to 252.2. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "yes" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case29/case29_40.json b/sleep_assessment/cases/case29/case29_40.json deleted file mode 100644 index ef8bebc423ba0ca4ae434fcb37e100bd443f3cd0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case29/case29_40.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "case29_40_SHHS1_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Please analyze the provided sleep file and determine whether arousal occurs in each of the following five 60-second sleep segments: A = minute 510.0 to 511.0, B = minute 322.55 to 323.55, C = minute 436.31 to 437.31, D = minute 479.46 to 480.46, E = minute 541.0 to 542.0. Report which segment labels contain arousal and which segment labels do not contain arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract whether each segment A, B, C, D, and E is reported as containing arousal.\nIf the report lists arousal segments and non-arousal segments as groups, convert the groups into one yes/no value for each segment.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"yes\", \"no\", or null.\n6. Use \"yes\" if the segment is reported as containing arousal.\n7. Use \"no\" if the segment is reported as not containing arousal.\n8. If a segment's status is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"yes\", \"B\": \"no\", \"C\": \"yes\", \"D\": \"no\", \"E\": \"no\"}", - "metrics": [ - { - "metric_id": "segment_A_arousal_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_arousal_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_arousal_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_arousal_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_arousal_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_01.json b/sleep_assessment/cases/case3/case3_01.json deleted file mode 100644 index ecbda64beb7db99bfa5daa2ba88e5c8f419f2345..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_01.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 308.0, - "tolerance": 9.24 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 440.0, - "tolerance": 13.2 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 70.0, - "tolerance": 2.1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_02.json b/sleep_assessment/cases/case3/case3_02.json deleted file mode 100644 index be3ccd63143bfc55e34995dd6324b21f958eb42f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_02.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 359.5, - "tolerance": 10.785 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 482.0, - "tolerance": 14.459999999999999 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 74.5850622406639, - "tolerance": 2.237551867219917 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_03.json b/sleep_assessment/cases/case3/case3_03.json deleted file mode 100644 index 8fd9ea099fbae464fb0a785d21fe9f0ae817c81f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_03.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 405.5, - "tolerance": 12.165 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 471.5, - "tolerance": 14.145 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 86.00212089077412, - "tolerance": 2.5800636267232235 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_04.json b/sleep_assessment/cases/case3/case3_04.json deleted file mode 100644 index bdc7c36f2047d0f3257a69249f9c34d43e656b32..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_04.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 467.5, - "tolerance": 14.025 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 481.5, - "tolerance": 14.445 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 97.09241952232607, - "tolerance": 2.912772585669782 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_05.json b/sleep_assessment/cases/case3/case3_05.json deleted file mode 100644 index ac969ce28d0d104a53c879b57b754de684318222..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_05.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 289.5, - "tolerance": 8.685 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 437.5, - "tolerance": 13.125 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 66.17142857142856, - "tolerance": 1.9851428571428569 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_06.json b/sleep_assessment/cases/case3/case3_06.json deleted file mode 100644 index 82998aa1873ceb5a25ebc406e4ff66eb8ea59135..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_06.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 351.5, - "tolerance": 10.545 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 427.0, - "tolerance": 12.809999999999999 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 82.31850117096019, - "tolerance": 2.469555035128806 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_07.json b/sleep_assessment/cases/case3/case3_07.json deleted file mode 100644 index 61a74522dfd4e188245b5086d018a68d5cb1df78..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_07.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 387.0, - "tolerance": 11.61 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 428.0, - "tolerance": 12.84 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 90.42056074766354, - "tolerance": 2.712616822429906 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_08.json b/sleep_assessment/cases/case3/case3_08.json deleted file mode 100644 index c16f03a94da896bf0612bfc8d49e6c6bf5df18ad..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_08.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 275.5, - "tolerance": 8.265 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 477.0, - "tolerance": 14.309999999999999 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 57.75681341719078, - "tolerance": 1.7327044025157232 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_09.json b/sleep_assessment/cases/case3/case3_09.json deleted file mode 100644 index de8af8acac3fab1a4652fa580bc3827e9e127304..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_09.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 468.0, - "tolerance": 14.04 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 508.0, - "tolerance": 15.24 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 92.1259842519685, - "tolerance": 2.763779527559055 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_10.json b/sleep_assessment/cases/case3/case3_10.json deleted file mode 100644 index 17915d328d9fac960796e9d09cb13cba37a50b20..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_10.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 379.0, - "tolerance": 11.37 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 479.5, - "tolerance": 14.385 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 79.04066736183525, - "tolerance": 2.3712200208550573 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_11.json b/sleep_assessment/cases/case3/case3_11.json deleted file mode 100644 index 19dae0da2f64a95f19e8a64ec99ca88abcfe82e8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_11.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 375.5, - "tolerance": 11.264999999999999 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 542.0, - "tolerance": 16.259999999999998 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 69.28044280442805, - "tolerance": 2.0784132841328415 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_12.json b/sleep_assessment/cases/case3/case3_12.json deleted file mode 100644 index 481a41a4c46a260c7db5cbf971f7d1f4bd02de90..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_12.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 182.0, - "tolerance": 5.46 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 539.5, - "tolerance": 16.185 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 33.734939759036145, - "tolerance": 1.0120481927710843 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_13.json b/sleep_assessment/cases/case3/case3_13.json deleted file mode 100644 index 3dea68ed8fb9f912731c5e6c19e46ce997cad466..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_13.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 358.5, - "tolerance": 10.754999999999999 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 524.5, - "tolerance": 15.735 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 68.35081029551954, - "tolerance": 2.0505243088655862 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_14.json b/sleep_assessment/cases/case3/case3_14.json deleted file mode 100644 index 41424c1e83657fe5bf367b48e30fd9f73c533f9f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_14.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 301.0, - "tolerance": 9.03 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 437.5, - "tolerance": 13.125 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 68.8, - "tolerance": 2.064 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_15.json b/sleep_assessment/cases/case3/case3_15.json deleted file mode 100644 index 24c468bda69cb2bd6310bbf9f7b8199e8e9fe243..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_15.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 370.0, - "tolerance": 11.1 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 542.0, - "tolerance": 16.259999999999998 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 68.26568265682657, - "tolerance": 2.047970479704797 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_16.json b/sleep_assessment/cases/case3/case3_16.json deleted file mode 100644 index 564edfe983c7d9b05c7b2b15c161a3d05c76e951..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_16.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 469.5, - "tolerance": 14.084999999999999 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 504.0, - "tolerance": 15.12 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 93.15476190476191, - "tolerance": 2.794642857142857 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_17.json b/sleep_assessment/cases/case3/case3_17.json deleted file mode 100644 index ac4a79820cb874674b5dee59efeddca070853fa7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_17.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 477.5, - "tolerance": 14.325 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 536.0, - "tolerance": 16.08 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 89.08582089552239, - "tolerance": 2.6725746268656714 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_18.json b/sleep_assessment/cases/case3/case3_18.json deleted file mode 100644 index 470d6eddbcaf3b1675f365762096cdcd423d881f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_18.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 451.5, - "tolerance": 13.545 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 477.5, - "tolerance": 14.325 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 94.55497382198953, - "tolerance": 2.836649214659686 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_19.json b/sleep_assessment/cases/case3/case3_19.json deleted file mode 100644 index f69cd06329b6f305eb5271caceb333f09655f00c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_19.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 450.5, - "tolerance": 13.514999999999999 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 456.0, - "tolerance": 13.68 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 98.79385964912281, - "tolerance": 2.963815789473684 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_20.json b/sleep_assessment/cases/case3/case3_20.json deleted file mode 100644 index c21a7324b4f77f1c61787e2314c56e01b6fa62e5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_20.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 508.0, - "tolerance": 15.24 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 534.5, - "tolerance": 16.035 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 95.0420954162769, - "tolerance": 2.8512628624883067 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_21.json b/sleep_assessment/cases/case3/case3_21.json deleted file mode 100644 index 44cf9102a0c6892c1b78bfcfb83ff3cd2ca699d0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_21.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 239.0, - "tolerance": 7.17 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 422.5, - "tolerance": 12.674999999999999 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 56.5680473372781, - "tolerance": 1.697041420118343 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_22.json b/sleep_assessment/cases/case3/case3_22.json deleted file mode 100644 index a07fa9d2eb50c470624fdd42bba369d12fbf42ba..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_22.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 230.0, - "tolerance": 6.8999999999999995 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 388.5, - "tolerance": 11.655 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 59.202059202059196, - "tolerance": 1.7760617760617758 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_23.json b/sleep_assessment/cases/case3/case3_23.json deleted file mode 100644 index 0ada6b2af0ec0fb9fdf5434d633c01665432eb22..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_23.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 226.0, - "tolerance": 6.779999999999999 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 489.0, - "tolerance": 14.67 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 46.216768916155424, - "tolerance": 1.3865030674846626 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_24.json b/sleep_assessment/cases/case3/case3_24.json deleted file mode 100644 index 850058f023b3a77c2bbaeb321d2996e27b00d6d6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_24.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 381.5, - "tolerance": 11.445 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 422.0, - "tolerance": 12.66 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 90.40284360189574, - "tolerance": 2.712085308056872 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case3/case3_25.json b/sleep_assessment/cases/case3/case3_25.json deleted file mode 100644 index 67a12fb96300c0e839f51ab4ed5264625ac4eacc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case3/case3_25.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three values for the recording: Total Sleep Time (TST) in minutes, Total In Bed Time (TIB) in minutes, and Sleep Efficiency (SE) as a percentage. TIB must be computed from the label file duration. Please clearly report all three final values in your response." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) Total Sleep Time (TST) in minutes\n2) Total In Bed Time (TIB) in minutes\n3) Sleep Efficiency (SE) as a percentage\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys in the JSON must be EXACTLY \"tst_min\", \"tib_min\", and \"se_percent\".\n5. \"tst_min\" and \"tib_min\" must be FLOAT values in minutes or null.\n6. \"se_percent\" must be a FLOAT percentage value or null.\n7. If a value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"tst_min\": , \"tib_min\": , \"se_percent\": }", - "metrics": [ - { - "metric_id": "tst_accuracy", - "type": "numeric_check", - "target_key": "tst_min", - "weight": 30, - "params": { - "gt_value": 450.0, - "tolerance": 13.5 - } - }, - { - "metric_id": "tib_accuracy", - "type": "numeric_check", - "target_key": "tib_min", - "weight": 30, - "params": { - "gt_value": 479.5, - "tolerance": 14.385 - } - }, - { - "metric_id": "se_accuracy", - "type": "numeric_check", - "target_key": "se_percent", - "weight": 40, - "params": { - "gt_value": 93.84775808133472, - "tolerance": 2.8154327424400414 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_01.json b/sleep_assessment/cases/case30/case30_01.json deleted file mode 100644 index 9ad52c4cc88c8973784b1121e01032611439faf3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_01.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 182.75-183.75 minutes; B: 109.75-110.75 minutes; C: 22.5-23.5 minutes; D: 168.25-169.25 minutes; E: 81.5-82.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_02.json b/sleep_assessment/cases/case30/case30_02.json deleted file mode 100644 index 3b7f1917b1970193c265ccbe331678f176d77e03..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_02.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 225.25-226.25 minutes; B: 194.25-195.25 minutes; C: 141-142 minutes; D: 329.25-330.25 minutes; E: 295.5-296.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_03.json b/sleep_assessment/cases/case30/case30_03.json deleted file mode 100644 index 376bb5052b787da2b8b13bc8742fbcd422db33da..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_03.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 250.75-251.75 minutes; B: 344.25-345.25 minutes; C: 372.5-373.5 minutes; D: 417-418 minutes; E: 351.25-352.25 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "apnea" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_04.json b/sleep_assessment/cases/case30/case30_04.json deleted file mode 100644 index abbe6ee4714613e7e46b24b233a800666a57991f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_04.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 166.25-167.25 minutes; B: 123.75-124.75 minutes; C: 30.5-31.5 minutes; D: 131.75-132.75 minutes; E: 100-101 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_05.json b/sleep_assessment/cases/case30/case30_05.json deleted file mode 100644 index 31595b25ed6f2e09b109e0d61625e14ff21449de..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_05.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 146.75-147.75 minutes; B: 151.25-152.25 minutes; C: 188-189 minutes; D: 335.25-336.25 minutes; E: 252.5-253.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_06.json b/sleep_assessment/cases/case30/case30_06.json deleted file mode 100644 index 86f9cc485b52fbb4a18b870aa51b00a49631632e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_06.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 446.75-447.75 minutes; B: 480.25-481.25 minutes; C: 316-317 minutes; D: 381.5-382.5 minutes; E: 445-446 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_07.json b/sleep_assessment/cases/case30/case30_07.json deleted file mode 100644 index 105628d16c6687daf763e99b08b41fd731e76593..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_07.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 168.25-169.25 minutes; B: 259.25-260.25 minutes; C: 37-38 minutes; D: 263.75-264.75 minutes; E: 113.5-114.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_08.json b/sleep_assessment/cases/case30/case30_08.json deleted file mode 100644 index d80840863843c055e5091e49b8eb340b88c0fd0a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_08.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 372.75-373.75 minutes; B: 264.75-265.75 minutes; C: 191-192 minutes; D: 339.25-340.25 minutes; E: 273.5-274.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_09.json b/sleep_assessment/cases/case30/case30_09.json deleted file mode 100644 index 9ad47c3984a0073fb921df7e2ec0fff36a00f45a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_09.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 445.75-446.75 minutes; B: 362.75-363.75 minutes; C: 352-353 minutes; D: 394.75-395.75 minutes; E: 432-433 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_10.json b/sleep_assessment/cases/case30/case30_10.json deleted file mode 100644 index 73c98b5f42422cf62fe3f97c60adda249a15b804..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_10.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 194.25-195.25 minutes; B: 288.25-289.25 minutes; C: 33-34 minutes; D: 342.25-343.25 minutes; E: 100.5-101.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_11.json b/sleep_assessment/cases/case30/case30_11.json deleted file mode 100644 index da47ed06834be5d0d2720ef8b6794ba43e0537cb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_11.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 345.25-346.25 minutes; B: 348.25-349.25 minutes; C: 168-169 minutes; D: 349.25-350.25 minutes; E: 236.5-237.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_12.json b/sleep_assessment/cases/case30/case30_12.json deleted file mode 100644 index a0ef6184fa2384473586cafe7a4ae5ac34d14c95..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_12.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 300.75-301.75 minutes; B: 294.75-295.75 minutes; C: 309.5-310.5 minutes; D: 382.5-383.5 minutes; E: 448-449 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_13.json b/sleep_assessment/cases/case30/case30_13.json deleted file mode 100644 index 38b66221799c8c281e9cf40f29980e7b21376ab1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_13.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 355.75-356.75 minutes; B: 129.75-130.75 minutes; C: 31-32 minutes; D: 267.25-268.25 minutes; E: 97.5-98.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_14.json b/sleep_assessment/cases/case30/case30_14.json deleted file mode 100644 index 95b11a4e67b514467d1741bfd299c9a67fc7c758..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_14.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 361.75-362.75 minutes; B: 310.75-311.75 minutes; C: 164.5-165.5 minutes; D: 323.25-324.25 minutes; E: 231-232 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_15.json b/sleep_assessment/cases/case30/case30_15.json deleted file mode 100644 index 885f33018b8c2a001954068477b957df115c2663..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_15.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 348.25-349.25 minutes; B: 352.75-353.75 minutes; C: 302.5-303.5 minutes; D: 382.75-383.75 minutes; E: 406-407 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: X5 Snore is the snore signal; X6 Flow-1 is pressure-based airflow; DC3 Flow-2 is the second airflow signal; X7 and X8 Abdominal effort are abdominal respiratory-effort channels." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_16.json b/sleep_assessment/cases/case30/case30_16.json deleted file mode 100644 index 8dc0ba8aabcf397020d2433855c4b906c5662857..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_16.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 231.8-232.8 minutes; B: 80.51-81.51 minutes; C: 24.5-25.5 minutes; D: 142.92-143.92 minutes; E: 77.5-78.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_17.json b/sleep_assessment/cases/case30/case30_17.json deleted file mode 100644 index 0e64996b4113837afa827ebbc3b76af2b06f0d7d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_17.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 236.41-237.41 minutes; B: 311.25-312.25 minutes; C: 156.5-157.5 minutes; D: 350.75-351.75 minutes; E: 208-209 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_18.json b/sleep_assessment/cases/case30/case30_18.json deleted file mode 100644 index 43a572a2be43f932f38ebfc6c983df8745478869..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_18.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 412.35-413.35 minutes; B: 437.86-438.86 minutes; C: 276.5-277.5 minutes; D: 339.5-340.5 minutes; E: 403-404 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_19.json b/sleep_assessment/cases/case30/case30_19.json deleted file mode 100644 index d51e8415493b1666c4053adde1766532347e01aa..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_19.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 74.95-75.95 minutes; B: 47.25-48.25 minutes; C: 59.5-60.5 minutes; D: 112.14-113.14 minutes; E: 86.5-87.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_20.json b/sleep_assessment/cases/case30/case30_20.json deleted file mode 100644 index 70036560eb259955ae6227f92b722a123508077e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_20.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 75-76 minutes; B: 151.87-152.87 minutes; C: 118.5-119.5 minutes; D: 172.5-173.5 minutes; E: 157-158 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_21.json b/sleep_assessment/cases/case30/case30_21.json deleted file mode 100644 index 886270b163627c7d96802aa79a1cbd184fcca84b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_21.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 76.8-77.8 minutes; B: 197.02-198.02 minutes; C: 186-187 minutes; D: 223-224 minutes; E: 200.43-201.43 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "apnea" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_22.json b/sleep_assessment/cases/case30/case30_22.json deleted file mode 100644 index 94c337fe26338ed0d1bfe971d9af69a7631e4732..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_22.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 225.24-226.24 minutes; B: 144.84-145.84 minutes; C: 32-33 minutes; D: 227.25-228.25 minutes; E: 97-98 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_23.json b/sleep_assessment/cases/case30/case30_23.json deleted file mode 100644 index 9e2e16f01a199bbecee9211deb9054f6b57b6fd8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_23.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 254.78-255.78 minutes; B: 248.53-249.53 minutes; C: 172-173 minutes; D: 266.28-267.28 minutes; E: 294-295 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_24.json b/sleep_assessment/cases/case30/case30_24.json deleted file mode 100644 index 7614d6facefec6fbb5b622368f3f5b021f4d24c8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_24.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 259.73-260.73 minutes; B: 344.73-345.73 minutes; C: 386-387 minutes; D: 455.17-456.17 minutes; E: 472-473 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_25.json b/sleep_assessment/cases/case30/case30_25.json deleted file mode 100644 index f1eefebd1be208574906e1e933f81ea32b4fd681..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_25.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 299.34-300.34 minutes; B: 27.94-28.94 minutes; C: 48-49 minutes; D: 107.57-108.57 minutes; E: 126.5-127.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_26.json b/sleep_assessment/cases/case30/case30_26.json deleted file mode 100644 index f91d7acb48a9a742dd45d2da0936584dc677a2a0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_26.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 375.5-376.5 minutes; B: 175.84-176.84 minutes; C: 210-211 minutes; D: 281.85-282.85 minutes; E: 314.5-315.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_27.json b/sleep_assessment/cases/case30/case30_27.json deleted file mode 100644 index e64814384d7fff35f843910375f0042445d7df7c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_27.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 423.86-424.86 minutes; B: 321.09-322.09 minutes; C: 401.5-402.5 minutes; D: 377.91-378.91 minutes; E: 294.04-295.04 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "apnea" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_28.json b/sleep_assessment/cases/case30/case30_28.json deleted file mode 100644 index 89e89cc917ceb1ef7beea5d977510d7be3f7c35d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_28.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 146.53-147.53 minutes; B: 26.8-27.8 minutes; C: 29.5-30.5 minutes; D: 81.78-82.78 minutes; E: 98.5-99.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_29.json b/sleep_assessment/cases/case30/case30_29.json deleted file mode 100644 index bfe24d42b1f82684e5316ef97030c09a29c5c5c2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_29.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 156.25-157.25 minutes; B: 306.9-307.9 minutes; C: 171.5-172.5 minutes; D: 389.21-390.21 minutes; E: 225.5-226.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_30.json b/sleep_assessment/cases/case30/case30_30.json deleted file mode 100644 index f49f71a8674a14e9e2b66e7c2404966dfc3389b8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_30.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 416.52-417.52 minutes; B: 471.73-472.73 minutes; C: 278-279 minutes; D: 363.5-364.5 minutes; E: 434-435 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: THOR RES is thoracic respiratory effort; ABDO RES is abdominal respiratory effort; NEW AIR is nasal/oral airflow; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_31.json b/sleep_assessment/cases/case30/case30_31.json deleted file mode 100644 index dfcc099a5e1f6cef6659e94ae939b8ca322d20a8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_31.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 14.76-15.76 minutes; B: 66.2-67.2 minutes; C: 36.5-37.5 minutes; D: 116-117 minutes; E: 96-97 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_32.json b/sleep_assessment/cases/case30/case30_32.json deleted file mode 100644 index c0f70e4b09a578ebd3496ccaec3788948b2c6793..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_32.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 132-133 minutes; B: 165.65-166.65 minutes; C: 164-165 minutes; D: 166.96-167.96 minutes; E: 236-237 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_33.json b/sleep_assessment/cases/case30/case30_33.json deleted file mode 100644 index c20bdd443fb005977a3dad362454c9d6caf938dd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_33.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 144.55-145.55 minutes; B: 204.25-205.25 minutes; C: 295.5-296.5 minutes; D: 347-348 minutes; E: 273.78-274.78 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "apnea" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_34.json b/sleep_assessment/cases/case30/case30_34.json deleted file mode 100644 index 012a9b2ae0e4835824751590c59c224ff40232f0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_34.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 26.14-27.14 minutes; B: 48.8-49.8 minutes; C: 57-58 minutes; D: 153.75-154.75 minutes; E: 107-108 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_35.json b/sleep_assessment/cases/case30/case30_35.json deleted file mode 100644 index b3c0882782f5fc9c32da6a25f2ef745926ba0de7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_35.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 135.77-136.77 minutes; B: 208.75-209.75 minutes; C: 201-202 minutes; D: 256.41-257.41 minutes; E: 280-281 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_36.json b/sleep_assessment/cases/case30/case30_36.json deleted file mode 100644 index 35539df11139ea1b8c28934ca4ef14054aaf2a66..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_36.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 233.59-234.59 minutes; B: 330.25-331.25 minutes; C: 334-335 minutes; D: 260.42-261.42 minutes; E: 357.25-358.25 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "apnea" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_37.json b/sleep_assessment/cases/case30/case30_37.json deleted file mode 100644 index 0aa9ce42898bb7be9d95f586eb7271ea487ea50d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_37.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 47.75-48.75 minutes; B: 87.75-88.75 minutes; C: 97.5-98.5 minutes; D: 128.76-129.76 minutes; E: 244.5-245.5 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_38.json b/sleep_assessment/cases/case30/case30_38.json deleted file mode 100644 index a636ef142f68f6622b14fa5e61a5ec39bd16620f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_38.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 121.99-122.99 minutes; B: 229.66-230.66 minutes; C: 363.5-364.5 minutes; D: 298.51-299.51 minutes; E: 257.85-258.85 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "apnea" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_39.json b/sleep_assessment/cases/case30/case30_39.json deleted file mode 100644 index aacbb1f1ca43eb57e1e43ea3996d24682fef8a09..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_39.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 60.79-61.79 minutes; B: 37.42-38.42 minutes; C: 138.5-139.5 minutes; D: 140-141 minutes; E: 247-248 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "null" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case30/case30_40.json b/sleep_assessment/cases/case30/case30_40.json deleted file mode 100644 index b854b3eccf72de326bf19b5e04f8de29c4c98601..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case30/case30_40.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Given the sleep file, determine whether each of the following sleep segments contains an apnea event or a hypopnea event: A: 127.85-128.85 minutes; B: 279.22-280.22 minutes; C: 332.5-333.5 minutes; D: 380-381 minutes; E: 205.32-206.32 minutes. Report the corresponding event for segments A-E in order as apnea, hypopnea, or null.\n\nEach segment contains 60 seconds of sleep data. Prioritize airflow, respiratory-effort, and oxygen-saturation related channels when making the classification, and list the final A-E conclusions clearly.\n\nRespiratory channel notes: ABD is abdominal respiratory effort; CHEST is thoracic respiratory effort; AIRFLOW is the airflow signal; SaO2 is oxygen saturation." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the final event classification for each of five segments A, B, C, D, and E.\nNormalize the values as follows:\n- apnea: apnea, respiratory apnea, obstructive apnea, central apnea, mixed apnea\n- hypopnea: hypopnea, respiratory hypopnea\n- null: Null, null, none, no event, no apnea or hypopnea, no target respiratory event\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or extra fields.\n4. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n5. Each value must be exactly one of: \"apnea\", \"hypopnea\", or \"null\".\n6. If a segment's classification is missing or unclear, return the string \"null\" for that segment.\n7. Do not infer labels from explanations alone if no final A-E classification is stated clearly; return the string \"null\" for any unclear segment.\n\n### OUTPUT TEMPLATE\n{\"A\": \"apnea\", \"B\": \"hypopnea\", \"C\": \"null\", \"D\": \"apnea\", \"E\": \"null\"}", - "metrics": [ - { - "metric_id": "segment_A_event_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "apnea" - } - }, - { - "metric_id": "segment_B_event_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "hypopnea" - } - }, - { - "metric_id": "segment_C_event_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_D_event_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "null" - } - }, - { - "metric_id": "segment_E_event_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "apnea" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case31/case31_01.json b/sleep_assessment/cases/case31/case31_01.json deleted file mode 100644 index d3279499f821b579d0185ed11ab15abc21b44275..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_01.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 15.779220779220777, - "tolerance": 3.1558441558441555 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_02.json b/sleep_assessment/cases/case31/case31_02.json deleted file mode 100644 index f8e50c6090bf46f96b2b466425bd103036d8c8a4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_02.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 13.518776077885954, - "tolerance": 2.703755215577191 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_03.json b/sleep_assessment/cases/case31/case31_03.json deleted file mode 100644 index 03a8498f7d46024ca9dcf91c37c9e5c34fdd2467..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_03.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 14.500616522811344, - "tolerance": 2.900123304562269 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_04.json b/sleep_assessment/cases/case31/case31_04.json deleted file mode 100644 index 04de9491e3faf004d137c55bb157b4ca498945e3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_04.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 1.5401069518716577, - "tolerance": 0.30802139037433157 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_05.json b/sleep_assessment/cases/case31/case31_05.json deleted file mode 100644 index 280f4ac4d1f4359021d72acf896edcfc915ef5b0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_05.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 14.507772020725389, - "tolerance": 2.901554404145078 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_06.json b/sleep_assessment/cases/case31/case31_06.json deleted file mode 100644 index 3ae3d5cbc011d0e2436a5ca112121f22b203b8f6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_06.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 29.241011984021302, - "tolerance": 5.848202396804261 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_07.json b/sleep_assessment/cases/case31/case31_07.json deleted file mode 100644 index dbbd088b33192d7cd079639a138bed53a3304b8c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_07.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 30.65934065934066, - "tolerance": 6.131868131868132 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_08.json b/sleep_assessment/cases/case31/case31_08.json deleted file mode 100644 index 6cc381340e0a03a2f57197c3e537c577ba1e27af..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_08.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 27.280334728033473, - "tolerance": 5.456066945606695 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_09.json b/sleep_assessment/cases/case31/case31_09.json deleted file mode 100644 index 6b9c8189877bc8e37caa92f920232744b544e683..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_09.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 8.970099667774086, - "tolerance": 1.7940199335548173 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case31/case31_10.json b/sleep_assessment/cases/case31/case31_10.json deleted file mode 100644 index e7d3b27a8097444d2931bcfa9c85745b36105869..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case31/case31_10.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Arousal Index (ARI). Count the number of arousal events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. ARI is defined as arousal count divided by TST in hours, reported as arousal events per hour of total sleep time. Please clearly report the final ARI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Arousal Index (ARI) from the agent's report. ARI should be reported as arousal events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ari\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ari\": null}.\n\n### OUTPUT TEMPLATE\n{\"ari\": }", - "metrics": [ - { - "metric_id": "arousal_index_accuracy", - "type": "numeric_check", - "target_key": "ari", - "weight": 100, - "params": { - "gt_value": 20.756756756756754, - "tolerance": 4.151351351351351 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_01.json b/sleep_assessment/cases/case32/case32_01.json deleted file mode 100644 index 01ea0465431565480c0f344ea0031155bc5b0f8b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_01.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for ISRUC: X5 = snore signal; X6 = Flow-1, pressure-based airflow; DC3 = Flow-2, second airflow channel; X7 and X8 = abdominal respiratory-effort channels. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 32.14285714285714, - "tolerance": 6.428571428571428 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_02.json b/sleep_assessment/cases/case32/case32_02.json deleted file mode 100644 index 2470b1fb27b448bc3b82d5461200110839cbb60a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_02.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for ISRUC: X5 = snore signal; X6 = Flow-1, pressure-based airflow; DC3 = Flow-2, second airflow channel; X7 and X8 = abdominal respiratory-effort channels. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 5.674547983310154, - "tolerance": 1.1349095966620308 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_03.json b/sleep_assessment/cases/case32/case32_03.json deleted file mode 100644 index 21b85bd1efe25c825a3d1d4e8191aa50904c8221..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_03.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for ISRUC: X5 = snore signal; X6 = Flow-1, pressure-based airflow; DC3 = Flow-2, second airflow channel; X7 and X8 = abdominal respiratory-effort channels. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 1.7755856966707768, - "tolerance": 0.3551171393341554 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_04.json b/sleep_assessment/cases/case32/case32_04.json deleted file mode 100644 index 5d6c467b7ff9f505ddccaeb9a33e7369c84653b6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_04.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for ISRUC: X5 = snore signal; X6 = Flow-1, pressure-based airflow; DC3 = Flow-2, second airflow channel; X7 and X8 = abdominal respiratory-effort channels. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 1.1550802139037433, - "tolerance": 0.23101604278074866 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_05.json b/sleep_assessment/cases/case32/case32_05.json deleted file mode 100644 index cc1c0f9c205197301cd640a7a5b012cd362b768f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_05.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for ISRUC: X5 = snore signal; X6 = Flow-1, pressure-based airflow; DC3 = Flow-2, second airflow channel; X7 and X8 = abdominal respiratory-effort channels. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 8.911917098445596, - "tolerance": 1.7823834196891193 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_06.json b/sleep_assessment/cases/case32/case32_06.json deleted file mode 100644 index 6c77e721e92a99d3185abf2cbdc2501d431a82a5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_06.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 13.901464713715045, - "tolerance": 2.780292942743009 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_07.json b/sleep_assessment/cases/case32/case32_07.json deleted file mode 100644 index 754ae374c7a91fd875306543886636b80f5c3426..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_07.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 58.02197802197802, - "tolerance": 11.604395604395606 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_08.json b/sleep_assessment/cases/case32/case32_08.json deleted file mode 100644 index 426e73f1885a7465a54963ce00d7f0ce590a12cf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_08.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 18.410041841004187, - "tolerance": 3.6820083682008375 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_09.json b/sleep_assessment/cases/case32/case32_09.json deleted file mode 100644 index 30013975c9b9a6cd789d15d62253c1343256eafa..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_09.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 25.714285714285715, - "tolerance": 5.142857142857143 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_10.json b/sleep_assessment/cases/case32/case32_10.json deleted file mode 100644 index 81259d1ede59130f43aac614bafaa4f54f1e356c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_10.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 15.72972972972973, - "tolerance": 3.1459459459459462 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_11.json b/sleep_assessment/cases/case32/case32_11.json deleted file mode 100644 index 5f0a43b63eb09ec268f2fe668bd91f00b1071db1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_11.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 10.794979079497908, - "tolerance": 2.1589958158995817 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_12.json b/sleep_assessment/cases/case32/case32_12.json deleted file mode 100644 index 2192eb80b6d1e57a712b941d9840b6e966cc0d7e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_12.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 39.91304347826087, - "tolerance": 7.982608695652174 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_13.json b/sleep_assessment/cases/case32/case32_13.json deleted file mode 100644 index b9e60222899d2adcd33be965a07b35a73c0a918f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_13.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 0.7964601769911505, - "tolerance": 0.15929203539823011 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_14.json b/sleep_assessment/cases/case32/case32_14.json deleted file mode 100644 index 492d276b74eb28c6bd026be34512d4faff6a7ed1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_14.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 29.0956749672346, - "tolerance": 5.81913499344692 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case32/case32_15.json b/sleep_assessment/cases/case32/case32_15.json deleted file mode 100644 index ed2a0dc5dadcdd6e49fda5e32bb7f1480abfe956..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case32/case32_15.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Apnea-Hypopnea Index (AHI). Count all apnea events and all hypopnea events across the full recording from the sleep signal. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds, converted to hours. AHI is defined as (apnea_count + hypopnea_count) divided by TST in hours, reported as events per hour of total sleep time. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final AHI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the whole-night Apnea-Hypopnea Index (AHI) from the agent's report. AHI should be reported as apnea plus hypopnea events per hour of total sleep time.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"ahi\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"ahi\": null}.\n\n### OUTPUT TEMPLATE\n{\"ahi\": }", - "metrics": [ - { - "metric_id": "apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "ahi", - "weight": 100, - "params": { - "gt_value": 32.666666666666664, - "tolerance": 6.533333333333333 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_01.json b/sleep_assessment/cases/case33/case33_01.json deleted file mode 100644 index 44ff81a8d8ac01208f7b2b8881fdb896a8652024..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_01.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Respiratory Disturbance Index (RDI). Count all apnea events, hypopnea events, and respiratory-effort-related arousal (RERA) events across the full recording from the sleep signals. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds and converted to hours. RDI is defined as (apnea_count + hypopnea_count + RERA_count) divided by TST in hours, reported as events per hour. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final RDI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night Respiratory Disturbance Index (RDI).\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The key must be EXACTLY \"rdi\".\n4. The value must be a FLOAT or null.\n5. If the value is missing or unclear, return {\"rdi\": null}.\n\n### OUTPUT TEMPLATE\n{\"rdi\": }", - "metrics": [ - { - "metric_id": "respiratory_disturbance_index_accuracy", - "type": "numeric_check", - "target_key": "rdi", - "weight": 100, - "params": { - "gt_value": 17.573221757322177, - "tolerance": 3.5146443514644354 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_02.json b/sleep_assessment/cases/case33/case33_02.json deleted file mode 100644 index 6138d7cd588abfa06dbcf4f9286f72e07c531516..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_02.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Respiratory Disturbance Index (RDI). Count all apnea events, hypopnea events, and respiratory-effort-related arousal (RERA) events across the full recording from the sleep signals. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds and converted to hours. RDI is defined as (apnea_count + hypopnea_count + RERA_count) divided by TST in hours, reported as events per hour. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final RDI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night Respiratory Disturbance Index (RDI).\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The key must be EXACTLY \"rdi\".\n4. The value must be a FLOAT or null.\n5. If the value is missing or unclear, return {\"rdi\": null}.\n\n### OUTPUT TEMPLATE\n{\"rdi\": }", - "metrics": [ - { - "metric_id": "respiratory_disturbance_index_accuracy", - "type": "numeric_check", - "target_key": "rdi", - "weight": 100, - "params": { - "gt_value": 51.391304347826086, - "tolerance": 10.278260869565218 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_03.json b/sleep_assessment/cases/case33/case33_03.json deleted file mode 100644 index 6ba7721117e20a49bd59889ae2daf6642e86e09d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_03.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Respiratory Disturbance Index (RDI). Count all apnea events, hypopnea events, and respiratory-effort-related arousal (RERA) events across the full recording from the sleep signals. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds and converted to hours. RDI is defined as (apnea_count + hypopnea_count + RERA_count) divided by TST in hours, reported as events per hour. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final RDI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night Respiratory Disturbance Index (RDI).\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The key must be EXACTLY \"rdi\".\n4. The value must be a FLOAT or null.\n5. If the value is missing or unclear, return {\"rdi\": null}.\n\n### OUTPUT TEMPLATE\n{\"rdi\": }", - "metrics": [ - { - "metric_id": "respiratory_disturbance_index_accuracy", - "type": "numeric_check", - "target_key": "rdi", - "weight": 100, - "params": { - "gt_value": 1.0619469026548674, - "tolerance": 0.21238938053097348 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_04.json b/sleep_assessment/cases/case33/case33_04.json deleted file mode 100644 index 108080a128ad1943275a5fd7e76cccc1883a6f42..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_04.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Respiratory Disturbance Index (RDI). Count all apnea events, hypopnea events, and respiratory-effort-related arousal (RERA) events across the full recording from the sleep signals. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds and converted to hours. RDI is defined as (apnea_count + hypopnea_count + RERA_count) divided by TST in hours, reported as events per hour. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final RDI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night Respiratory Disturbance Index (RDI).\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The key must be EXACTLY \"rdi\".\n4. The value must be a FLOAT or null.\n5. If the value is missing or unclear, return {\"rdi\": null}.\n\n### OUTPUT TEMPLATE\n{\"rdi\": }", - "metrics": [ - { - "metric_id": "respiratory_disturbance_index_accuracy", - "type": "numeric_check", - "target_key": "rdi", - "weight": 100, - "params": { - "gt_value": 32.870249017038006, - "tolerance": 6.574049803407601 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_05.json b/sleep_assessment/cases/case33/case33_05.json deleted file mode 100644 index 9ac5098562a008302fe398b4a1eec3708850f908..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_05.json +++ /dev/null @@ -1,28 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate the whole-night Respiratory Disturbance Index (RDI). Count all apnea events, hypopnea events, and respiratory-effort-related arousal (RERA) events across the full recording from the sleep signals. Compute total sleep time (TST) from the label file as the number of non-Wake epochs (N1, N2, N3, or R/REM) multiplied by 30 seconds and converted to hours. RDI is defined as (apnea_count + hypopnea_count + RERA_count) divided by TST in hours, reported as events per hour. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Please clearly report the final RDI value." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night Respiratory Disturbance Index (RDI).\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The key must be EXACTLY \"rdi\".\n4. The value must be a FLOAT or null.\n5. If the value is missing or unclear, return {\"rdi\": null}.\n\n### OUTPUT TEMPLATE\n{\"rdi\": }", - "metrics": [ - { - "metric_id": "respiratory_disturbance_index_accuracy", - "type": "numeric_check", - "target_key": "rdi", - "weight": 100, - "params": { - "gt_value": 37.86666666666667, - "tolerance": 7.573333333333334 - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_06.json b/sleep_assessment/cases/case33/case33_06.json deleted file mode 100644 index a595226e714f9d9e8e079ec9edcaaf3dcd1dde33..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_06.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 66.92-67.92 minutes; B: 10.01-11.01 minutes; C: 77.7-78.7 minutes; D: 151.59-152.59 minutes; E: 86.5-87.5 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 16, - "params": { - "gt_value": "no" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_07.json b/sleep_assessment/cases/case33/case33_07.json deleted file mode 100644 index 991f95c6bdc1bb0461fd069c96b1e2cc7b9447cf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_07.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 144.55-145.55 minutes; B: 115.08-116.08 minutes; C: 166.89-167.89 minutes; D: 188.5-189.5 minutes; E: 238.42-239.42 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 16, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 25, - "params": { - "gt_value": "yes" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_08.json b/sleep_assessment/cases/case33/case33_08.json deleted file mode 100644 index d03b5a094e479684dfe3fb1b703c5a9580f76412..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_08.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 127.68-128.68 minutes; B: 23.94-24.94 minutes; C: 250.04-251.04 minutes; D: 130.66-131.66 minutes; E: 200-201 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 16, - "params": { - "gt_value": "no" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_09.json b/sleep_assessment/cases/case33/case33_09.json deleted file mode 100644 index 29ed90a8104733db417d7e9f61c573bd1241056d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_09.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 320.3-321.3 minutes; B: 251.74-252.74 minutes; C: 291.5-292.5 minutes; D: 305.98-306.98 minutes; E: 367.31-368.31 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 16, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 25, - "params": { - "gt_value": "yes" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_10.json b/sleep_assessment/cases/case33/case33_10.json deleted file mode 100644 index a64a9539c8048f485837e629f79f7a8da869bdc1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_10.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 16.52-17.52 minutes; B: 23.43-24.43 minutes; C: 132.98-133.98 minutes; D: 82.5-83.5 minutes; E: 270.5-271.5 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_11.json b/sleep_assessment/cases/case33/case33_11.json deleted file mode 100644 index 0fb54c880f94af5327eeb5a53736d8845774e8d2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_11.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 26.95-27.95 minutes; B: 4-5 minutes; C: 59-60 minutes; D: 294.5-295.5 minutes; E: 397.5-398.5 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 20, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 20, - "params": { - "gt_value": "no" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_12.json b/sleep_assessment/cases/case33/case33_12.json deleted file mode 100644 index 8d71e111661b08e947ae23941669b4715db57c5b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_12.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 33.16-34.16 minutes; B: 85.14-86.14 minutes; C: 55.52-56.52 minutes; D: 165.98-166.98 minutes; E: 193.5-194.5 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 16, - "params": { - "gt_value": "no" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_13.json b/sleep_assessment/cases/case33/case33_13.json deleted file mode 100644 index c2054eb674066ca7855ca198fa18ba723a695ccd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_13.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 108.17-109.17 minutes; B: 121.99-122.99 minutes; C: 181-182 minutes; D: 359.5-360.5 minutes; E: 370.41-371.41 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 16, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 25, - "params": { - "gt_value": "yes" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_14.json b/sleep_assessment/cases/case33/case33_14.json deleted file mode 100644 index e74e6b0eb28d85487754cb83dc1a4b61b7bf5bc7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_14.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 60.79-61.79 minutes; B: 17.04-18.04 minutes; C: 50.8-51.8 minutes; D: 159.49-160.49 minutes; E: 243.5-244.5 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 16, - "params": { - "gt_value": "no" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case33/case33_15.json b/sleep_assessment/cases/case33/case33_15.json deleted file mode 100644 index 649b65f7bbac6e500ba4e3c00487cd38399ad1ab..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case33/case33_15.json +++ /dev/null @@ -1,62 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "instruction": "Given the sleep file, determine whether a respiratory-effort-related arousal (RERA) event is present in each of the following five 60-second sleep segments: A: 178.75-179.75 minutes; B: 330.02-331.02 minutes; C: 183.95-184.95 minutes; D: 173.98-174.98 minutes; E: 381.5-382.5 minutes. Report yes or no for segments A-E in order. Distinguish RERA from apnea, hypopnea, and segments without a target respiratory event. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract whether RERA is present in segments A, B, C, D, and E.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\", \"B\", \"C\", \"D\", and \"E\".\n4. Each value must be exactly \"yes\", \"no\", or null.\n5. Normalize affirmative RERA conclusions to \"yes\" and absent/no-RERA conclusions to \"no\".\n6. If a segment is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"yes\",\"B\":\"no\",\"C\":\"no\",\"D\":\"yes\",\"E\":\"no\"}", - "metrics": [ - { - "metric_id": "segment_A_rera_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_B_rera_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 25, - "params": { - "gt_value": "yes" - } - }, - { - "metric_id": "segment_C_rera_accuracy", - "type": "categorical_check", - "target_key": "C", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_D_rera_accuracy", - "type": "categorical_check", - "target_key": "D", - "weight": 17, - "params": { - "gt_value": "no" - } - }, - { - "metric_id": "segment_E_rera_accuracy", - "type": "categorical_check", - "target_key": "E", - "weight": 16, - "params": { - "gt_value": "no" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case34/case34_01.json b/sleep_assessment/cases/case34/case34_01.json deleted file mode 100644 index 48b7b598ad843868e36bc4a86f6f41a8c9bd738b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_01.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 40.70000000000073, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 224.34666666666666, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case34/case34_02.json b/sleep_assessment/cases/case34/case34_02.json deleted file mode 100644 index bc0b401c6a9d5904a57e8af84845792c46ed08e2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_02.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 26.100000000000364, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 250.91833333333335, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case34/case34_03.json b/sleep_assessment/cases/case34/case34_03.json deleted file mode 100644 index df536a98b980c5227bbe312e112c10482ac4b6e5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_03.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 24.5, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 70.6, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case34/case34_04.json b/sleep_assessment/cases/case34/case34_04.json deleted file mode 100644 index ec66a85219b975fee97b6692c20a5120e4650c9a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_04.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 30.900000000000546, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 114.90816666666666, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case34/case34_05.json b/sleep_assessment/cases/case34/case34_05.json deleted file mode 100644 index df2aa9410f57853f8141bcfae7cebd710f848115..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_05.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 39.399999999999636, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 135.77333333333334, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case34/case34_06.json b/sleep_assessment/cases/case34/case34_06.json deleted file mode 100644 index 8848f1689bdd4cd280fbbdd1e99a6821793a6087..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_06.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 58.70000000000073, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 145.27333333333334, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case34/case34_07.json b/sleep_assessment/cases/case34/case34_07.json deleted file mode 100644 index 8552d3b5315c9d8702c94bfc4b8b031ddf2894f8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_07.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 31.299999999999272, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 231.87833333333336, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case34/case34_08.json b/sleep_assessment/cases/case34/case34_08.json deleted file mode 100644 index 5ed2d2b775188ef046b66619d7c9088e573ac3a5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_08.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 38.0, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 384.98333333333335, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case34/case34_09.json b/sleep_assessment/cases/case34/case34_09.json deleted file mode 100644 index d572745cd0d18632f89e20e9855aa363790da9c2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case34/case34_09.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep EEG/PSG file and paired sleep-stage label file to identify the apnea event with the longest duration across the whole recording. Report its duration in seconds, its start time in minutes from the beginning of the recording, and the sleep stage at the event start. The sleep-stage output must be one of W, N1, N2, N3, or R. Respiratory channels: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the duration in seconds, start time in minutes, and sleep stage of the longest apnea event.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"longest_apnea_duration_sec\", \"longest_apnea_start_min\", and \"sleep_stage\".\n4. The two numeric values must be FLOATS or null.\n5. \"sleep_stage\" must be exactly W, N1, N2, N3, R, or null.\n6. Normalize REM to R.\n7. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"longest_apnea_duration_sec\": , \"longest_apnea_start_min\": , \"sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "longest_apnea_duration_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_duration_sec", - "weight": 40, - "params": { - "gt_value": 23.399999999999636, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_start_time_accuracy", - "type": "numeric_check", - "target_key": "longest_apnea_start_min", - "weight": 40, - "params": { - "gt_value": 146.82999999999998, - "tolerance": 1.0 - } - }, - { - "metric_id": "longest_apnea_sleep_stage_accuracy", - "type": "categorical_check", - "target_key": "sleep_stage", - "weight": 20, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_01.json b/sleep_assessment/cases/case35/case35_01.json deleted file mode 100644 index c0e78fa293d73e0263cb04666bcf6a5d757f38c3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_01.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 50.75-51.75 minutes; B: 137.25-138.25 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_02.json b/sleep_assessment/cases/case35/case35_02.json deleted file mode 100644 index 531ade8db4a6417b5b412f30a677e9ac0bbe434c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_02.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 261-262 minutes; B: 53.75-54.75 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "MA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "CA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_03.json b/sleep_assessment/cases/case35/case35_03.json deleted file mode 100644 index 16cdcaa8572da90b9f2da09144edc073baf6e368..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_03.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 167.25-168.25 minutes; B: 263.5-264.5 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "MA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_04.json b/sleep_assessment/cases/case35/case35_04.json deleted file mode 100644 index 8d4c41be0ba2f07b18ce35567b1f0ca6a133c988..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_04.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 210.75-211.75 minutes; B: 172.75-173.75 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_05.json b/sleep_assessment/cases/case35/case35_05.json deleted file mode 100644 index 527ae8b3a1645f4ac7ae12a2a786aad2a1cfc02f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_05.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 264-265 minutes; B: 213.75-214.75 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "MA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "CA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_06.json b/sleep_assessment/cases/case35/case35_06.json deleted file mode 100644 index f19053ca0c205f85c6feb3544896e062ccc7ffc9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_06.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 182.75-183.75 minutes; B: 265.5-266.5 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "MA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_07.json b/sleep_assessment/cases/case35/case35_07.json deleted file mode 100644 index de968352b5b959bc9608f2aa807bd857b10ca10e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_07.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 225.25-226.25 minutes; B: 192.25-193.25 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_08.json b/sleep_assessment/cases/case35/case35_08.json deleted file mode 100644 index d3ba676d0ab2c1d499450d2092017ce32676d6d4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_08.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 267-268 minutes; B: 236.75-237.75 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "MA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "CA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_09.json b/sleep_assessment/cases/case35/case35_09.json deleted file mode 100644 index 4004e660612bb683996c02aa64664fc24144154a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_09.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 198.75-199.75 minutes; B: 271-272 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "MA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_10.json b/sleep_assessment/cases/case35/case35_10.json deleted file mode 100644 index 141434999e98d46d788de2fe6bb12263f337f45e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_10.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 240.75-241.75 minutes; B: 346.75-347.75 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_11.json b/sleep_assessment/cases/case35/case35_11.json deleted file mode 100644 index 8f34c815ed5d7cb3950b83141c8a8393e6455c04..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_11.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 168.25-169.25 minutes; B: 372.75-373.75 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "CA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_12.json b/sleep_assessment/cases/case35/case35_12.json deleted file mode 100644 index 8cdf1a1f26218b812e02a0d2b59c70554f358ad5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_12.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 355.75-356.75 minutes; B: 361.75-362.75 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_13.json b/sleep_assessment/cases/case35/case35_13.json deleted file mode 100644 index 825f5f5aa516a28e2e34e20cd8105479386d3997..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_13.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 4.0053-5.0053 minutes; B: 108.0262-109.0262 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_14.json b/sleep_assessment/cases/case35/case35_14.json deleted file mode 100644 index abc02e86d1b93bb9adf5e09e319c398d21bf5895..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_14.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 8.0337-9.0337 minutes; B: 125.9338-126.9338 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_15.json b/sleep_assessment/cases/case35/case35_15.json deleted file mode 100644 index e251a15d3c490ab6d72f62d105f83245d27c12fb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_15.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 8.883-9.883 minutes; B: 134.8967-135.8967 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_16.json b/sleep_assessment/cases/case35/case35_16.json deleted file mode 100644 index 080f4547e2f3e379054d30381fd972e6b4d2b023..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_16.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 18.4128-19.4128 minutes; B: 136.317-137.317 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_17.json b/sleep_assessment/cases/case35/case35_17.json deleted file mode 100644 index 4e7b3184a28db97866ae89b91401f60f8ebd9317..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_17.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 21.4165-22.4165 minutes; B: 137.6913-138.6913 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_18.json b/sleep_assessment/cases/case35/case35_18.json deleted file mode 100644 index 844049783e54ec88418694a0df61b1c0efcec256..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_18.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 130.2753-131.2753 minutes; B: 138.5528-139.5528 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_19.json b/sleep_assessment/cases/case35/case35_19.json deleted file mode 100644 index e1c9b817262ecef86477c8503e6326bc733c8183..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_19.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 208.4252-209.4252 minutes; B: 222.8902-223.8902 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_20.json b/sleep_assessment/cases/case35/case35_20.json deleted file mode 100644 index 5eb9a8376e6ff57c1d96786b79caf113987d31e6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_20.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 266.2477-267.2477 minutes; B: 231.1058-232.1058 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_21.json b/sleep_assessment/cases/case35/case35_21.json deleted file mode 100644 index 38d13dd7c8222cf62b0d1c58c3e9494c4c9ecfa2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_21.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 158.2463-159.2463 minutes; B: 13.1352-14.1352 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_22.json b/sleep_assessment/cases/case35/case35_22.json deleted file mode 100644 index 609da6b9b25f1e72c10b0d897ef8ed959f92a775..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_22.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 220.9055-221.9055 minutes; B: 17.0533-18.0533 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_23.json b/sleep_assessment/cases/case35/case35_23.json deleted file mode 100644 index 3d618091363bf3f285f639d9fce291aae4518946..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_23.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 224.4773-225.4773 minutes; B: 31.8415-32.8415 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_24.json b/sleep_assessment/cases/case35/case35_24.json deleted file mode 100644 index 3e4cf625eb9a1007aab0f9c68a23051abe80df02..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_24.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 225.3435-226.3435 minutes; B: 41.4067-42.4067 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_25.json b/sleep_assessment/cases/case35/case35_25.json deleted file mode 100644 index 0293c4a7c8e89b34a14bb44f84a70b115a9e2ba1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_25.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 226.4082-227.4082 minutes; B: 117.7177-118.7177 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_26.json b/sleep_assessment/cases/case35/case35_26.json deleted file mode 100644 index a9e1e84d93581532ac64e7ef4c4334d6287aa821..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_26.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 237.0553-238.0553 minutes; B: 147.5847-148.5847 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_27.json b/sleep_assessment/cases/case35/case35_27.json deleted file mode 100644 index 52145be00ae1a7c4e2bf73c0a7643d0214af135c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_27.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 237.7278-238.7278 minutes; B: 154.952-155.952 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_28.json b/sleep_assessment/cases/case35/case35_28.json deleted file mode 100644 index 7c6a35f56ab9dcc9f940a26cd91e37ee0515bc46..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_28.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 189.9237-190.9237 minutes; B: 243.536-244.536 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_29.json b/sleep_assessment/cases/case35/case35_29.json deleted file mode 100644 index 783d09461e494f538241d3ff8d2951048d1bafd3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_29.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 311.2957-312.2957 minutes; B: 348.2958-349.2958 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_30.json b/sleep_assessment/cases/case35/case35_30.json deleted file mode 100644 index 01c12976026771339ffca4066739803cedc0b6c7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_30.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 8.8767-9.8767 minutes; B: 3.1475-4.1475 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_31.json b/sleep_assessment/cases/case35/case35_31.json deleted file mode 100644 index 216fbe945e9b29b1b2dcf2cd8b839789a454ca71..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_31.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 13.4398-14.4398 minutes; B: 4.93-5.93 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_32.json b/sleep_assessment/cases/case35/case35_32.json deleted file mode 100644 index 1680d12d4f63e7044c13dba927bcbb207caf0417..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_32.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 19.1613-20.1613 minutes; B: 18.3727-19.3727 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_33.json b/sleep_assessment/cases/case35/case35_33.json deleted file mode 100644 index 6607600989bf42cee4319a35cb0aab1e30a9ccb6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_33.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 20.998-21.998 minutes; B: 21.7163-22.7163 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_34.json b/sleep_assessment/cases/case35/case35_34.json deleted file mode 100644 index 1b037a7828afcc67637e7df75e40f52f44288e30..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_34.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 23.7058-24.7058 minutes; B: 33.8285-34.8285 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_35.json b/sleep_assessment/cases/case35/case35_35.json deleted file mode 100644 index 9a82fb1d11e4b4239b7927d7a452bca28f3c54ed..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_35.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 24.3633-25.3633 minutes; B: 70.3042-71.3042 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_36.json b/sleep_assessment/cases/case35/case35_36.json deleted file mode 100644 index 96dd4d6eaa381fc3bd7a98177b3d7b2942bb647f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_36.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 65.6665-66.6665 minutes; B: 107.9857-108.9857 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_37.json b/sleep_assessment/cases/case35/case35_37.json deleted file mode 100644 index cfaeeff8f056d283afbece68880b6df9b4f5165c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_37.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 146.6173-147.6173 minutes; B: 207.053-208.053 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_38.json b/sleep_assessment/cases/case35/case35_38.json deleted file mode 100644 index 9ec9c34bf1d132b5b6a78a8f2b48330f3f611b12..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_38.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 243.7145-244.7145 minutes; B: 363.5872-364.5872 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_39.json b/sleep_assessment/cases/case35/case35_39.json deleted file mode 100644 index 8d21edcca344dbaf1c9a05d5dc5c4ba74ad1329b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_39.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 176.952-177.952 minutes; B: 1.4788-2.4788 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_40.json b/sleep_assessment/cases/case35/case35_40.json deleted file mode 100644 index 0fa14b9c63c95fedf48a29899f68aa3af9029d0a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_40.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 271.4716-272.4716 minutes; B: 19.8838-20.8838 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_41.json b/sleep_assessment/cases/case35/case35_41.json deleted file mode 100644 index 6c79afd26e970fa5adb192598c5f0557fba96037..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_41.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 377.8767-378.8767 minutes; B: 53.7885-54.7885 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "CA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_42.json b/sleep_assessment/cases/case35/case35_42.json deleted file mode 100644 index c1cac8cb27a20eefa4c96b52748c72201fbabc9d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_42.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 67.2233-68.2233 minutes; B: 80.6448-81.6448 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_43.json b/sleep_assessment/cases/case35/case35_43.json deleted file mode 100644 index 92417f34b18382c70dc7b6ab9f77d41619131f50..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_43.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 95.7438-96.7438 minutes; B: 108.6482-109.6482 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_44.json b/sleep_assessment/cases/case35/case35_44.json deleted file mode 100644 index cfa4e900589bd6d7a76b1b594e0f515e58afbcaa..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_44.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 116.554-117.554 minutes; B: 120.8507-121.8507 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_45.json b/sleep_assessment/cases/case35/case35_45.json deleted file mode 100644 index 13024e1f2d24edc7a3469cb672d25d3c65df9a4f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_45.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 127.9563-128.9563 minutes; B: 135.8412-136.8412 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case35/case35_46.json b/sleep_assessment/cases/case35/case35_46.json deleted file mode 100644 index 691f2082533826809b98f01dd5c008ee81b50787..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case35/case35_46.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 159.0997-160.0997 minutes; B: 198.173-199.173 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", - "metrics": [ - { - "metric_id": "apnea_type_A_accuracy", - "type": "categorical_check", - "target_key": "A", - "weight": 50, - "params": { - "gt_value": "OA" - } - }, - { - "metric_id": "apnea_type_B_accuracy", - "type": "categorical_check", - "target_key": "B", - "weight": 50, - "params": { - "gt_value": "OA" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_01.json b/sleep_assessment/cases/case36/case36_01.json deleted file mode 100644 index 14123efc814f09a36ffad4e4061e71cdc6d2789c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_01.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 41.69491525423729, - "tolerance": 4.169491525423729 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 29.63855421686747, - "tolerance": 2.963855421686747 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_02.json b/sleep_assessment/cases/case36/case36_02.json deleted file mode 100644 index 75e14780dcbacf2002547826a414288818c9b2da..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_02.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 2.3529411764705883, - "tolerance": 0.23529411764705885 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 6.0291734197730955, - "tolerance": 0.6029173419773096 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_03.json b/sleep_assessment/cases/case36/case36_03.json deleted file mode 100644 index 3dcb564a8b04247d4e73cb9a01a0a4bfda2ad094..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_03.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 2.643171806167401, - "tolerance": 0.2643171806167401 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 1.4383561643835618, - "tolerance": 0.14383561643835618 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_04.json b/sleep_assessment/cases/case36/case36_04.json deleted file mode 100644 index dca099be2a446d0154c4ba2d797190c4713f852b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_04.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 2.608695652173913, - "tolerance": 0.2608695652173913 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 0.6808510638297872, - "tolerance": 0.06808510638297872 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_05.json b/sleep_assessment/cases/case36/case36_05.json deleted file mode 100644 index 171837b50c617c608ef73f804f16befd5165fb80..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_05.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 37.142857142857146, - "tolerance": 3.714285714285715 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 6.480446927374302, - "tolerance": 0.6480446927374303 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_06.json b/sleep_assessment/cases/case36/case36_06.json deleted file mode 100644 index 1879f0282a78c866341e1cfbf1917759338149ec..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_06.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 47.05882352941177, - "tolerance": 4.705882352941177 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 8.320493066255779, - "tolerance": 0.8320493066255779 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_07.json b/sleep_assessment/cases/case36/case36_07.json deleted file mode 100644 index 885142c8ee5de98eb316db666e7648c3ca3c22ba..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_07.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 55.08196721311476, - "tolerance": 5.508196721311476 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 55.84158415841584, - "tolerance": 5.584158415841585 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_08.json b/sleep_assessment/cases/case36/case36_08.json deleted file mode 100644 index f2733a5d8492d87b7cf041e20bd0aaca0697d8c4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_08.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 60.0, - "tolerance": 6.0 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 12.551505546751187, - "tolerance": 1.2551505546751187 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_09.json b/sleep_assessment/cases/case36/case36_09.json deleted file mode 100644 index 8549f0c7687ac756bde751f94160e85d3b5962f8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_09.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 69.13043478260869, - "tolerance": 6.913043478260869 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 12.941176470588236, - "tolerance": 1.2941176470588236 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_10.json b/sleep_assessment/cases/case36/case36_10.json deleted file mode 100644 index 749240ceff6e5b219de9f381aa8d2afa4bebbca8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_10.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 60.659340659340664, - "tolerance": 6.065934065934067 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 5.731895223420647, - "tolerance": 0.5731895223420648 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_11.json b/sleep_assessment/cases/case36/case36_11.json deleted file mode 100644 index ca7a165866e6ebf68a8cbb30643f0936c179038b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_11.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 10.881612090680102, - "tolerance": 1.0881612090680102 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_12.json b/sleep_assessment/cases/case36/case36_12.json deleted file mode 100644 index 200955e193a4f9fa79d584e59330055ff1a6c7e8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_12.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 13.333333333333334, - "tolerance": 1.3333333333333335 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 31.203566121842496, - "tolerance": 3.1203566121842496 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case36/case36_13.json b/sleep_assessment/cases/case36/case36_13.json deleted file mode 100644 index c7fc0fad2145d918d56d4a1cf6c7eec42abb987e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case36/case36_13.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Use the provided sleep EEG/PSG file and paired sleep-stage label file to calculate stage-specific Apnea-Hypopnea Indices for the whole recording. Assign each apnea or hypopnea event to the sleep stage at the event start. REM-AHI is the number of apnea plus hypopnea events starting during R/REM divided by REM sleep time in hours. NREM-AHI is the number starting during N1, N2, or N3 divided by NREM sleep time in hours. Events starting during Wake are excluded from both indices. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry. Report the final REM-AHI and NREM-AHI as events per hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final REM-specific AHI and NREM-specific AHI.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"rem_ahi\" and \"nrem_ahi\".\n4. Each value must be a FLOAT or null.\n5. Normalize REM AHI to rem_ahi and NREM AHI to nrem_ahi.\n6. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"rem_ahi\": , \"nrem_ahi\": }", - "metrics": [ - { - "metric_id": "rem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "rem_ahi", - "weight": 50, - "params": { - "gt_value": 18.823529411764707, - "tolerance": 1.8823529411764708 - } - }, - { - "metric_id": "nrem_apnea_hypopnea_index_accuracy", - "type": "numeric_check", - "target_key": "nrem_ahi", - "weight": 50, - "params": { - "gt_value": 34.43609022556391, - "tolerance": 3.443609022556391 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_01.json b/sleep_assessment/cases/case37/case37_01.json deleted file mode 100644 index 3784c95ebef6e5fbbfe6e563532a77e5114960af..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_01.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 173 to minute 174. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The analyzed 60-second window is 173-174 minutes. Ground truth: the first target event is a apnea annotation (OA) from 10380 to 10410 seconds, followed by an arousal from 10410 to 10440 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_02.json b/sleep_assessment/cases/case37/case37_02.json deleted file mode 100644 index 47884aafa75a484f8932fa5605d6a091e46c1e8b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_02.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 174.5 to minute 175.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The analyzed 60-second window is 174.5-175.5 minutes. Ground truth: the first target event is a hypopnea annotation (OH) from 10470 to 10500 seconds, followed by an arousal from 10500 to 10530 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_03.json b/sleep_assessment/cases/case37/case37_03.json deleted file mode 100644 index d5913ef3d57e2a8eb1fdc3de440121f6a4bff549..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_03.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 183 to minute 184. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The analyzed 60-second window is 183-184 minutes. Ground truth: the first target event is a apnea annotation (OA) from 10980 to 11010 seconds, followed by an arousal from 11010 to 11040 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_04.json b/sleep_assessment/cases/case37/case37_04.json deleted file mode 100644 index 6a5fdcc3900509cd4d950ca686aa44a53f5ca937..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_04.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 222.5 to minute 223.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The analyzed 60-second window is 222.5-223.5 minutes. Ground truth: the first target event is a apnea annotation (CA) from 13350 to 13380 seconds, followed by an arousal from 13380 to 13410 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_05.json b/sleep_assessment/cases/case37/case37_05.json deleted file mode 100644 index 3c138cb43ccfb5f2cac9408b2314f8b0afe1d5f7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_05.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 91.5 to minute 92.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The analyzed 60-second window is 91.5-92.5 minutes. Ground truth: the first target event is a hypopnea annotation (MH) from 5490 to 5520 seconds, followed by an arousal from 5520 to 5550 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_06.json b/sleep_assessment/cases/case37/case37_06.json deleted file mode 100644 index 8ca5fa4bcaa07dc956261e1b71c88800fbc48f83..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_06.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 93 to minute 94. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The analyzed 60-second window is 93-94 minutes. Ground truth: the first target event is a hypopnea annotation (MH) from 5580 to 5610 seconds, followed by an arousal from 5610 to 5640 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_07.json b/sleep_assessment/cases/case37/case37_07.json deleted file mode 100644 index b25ed038445a124a258211ebdf7703212e098139..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_07.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 132 to minute 133. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The analyzed 60-second window is 132-133 minutes. Ground truth: the first target event is a hypopnea annotation (MH) from 7920 to 7950 seconds, followed by an arousal from 7950 to 7980 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_08.json b/sleep_assessment/cases/case37/case37_08.json deleted file mode 100644 index 3012d08531475dff471d7d13c6b4fc5b7d6b70d1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_08.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 147 to minute 148. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The analyzed 60-second window is 147-148 minutes. Ground truth: the first target event is a hypopnea annotation (MH) from 8820 to 8850 seconds, followed by an arousal from 8850 to 8880 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_09.json b/sleep_assessment/cases/case37/case37_09.json deleted file mode 100644 index fcad02c8935cfd2fe60908f9b40702632e722ced..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_09.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 168.5 to minute 169.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The analyzed 60-second window is 168.5-169.5 minutes. Ground truth: the first target event is a apnea annotation (CA) from 10110 to 10140 seconds, followed by an arousal from 10140 to 10170 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_10.json b/sleep_assessment/cases/case37/case37_10.json deleted file mode 100644 index e067f021ddab5f9186526a51222587ce7d0393f7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_10.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 336 to minute 337. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The analyzed 60-second window is 336-337 minutes. Ground truth: the first target event is a hypopnea annotation (OH) from 20160 to 20190 seconds, followed by an arousal from 20190 to 20220 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_11.json b/sleep_assessment/cases/case37/case37_11.json deleted file mode 100644 index 5e976d46f5dbcc34acb045d1041cb49c6c8556b4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_11.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 339.5 to minute 340.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The analyzed 60-second window is 339.5-340.5 minutes. Ground truth: the first target event is a hypopnea annotation (OH) from 20370 to 20400 seconds, followed by an arousal from 20400 to 20430 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_12.json b/sleep_assessment/cases/case37/case37_12.json deleted file mode 100644 index ca0d913951fe562a5a3bb48bda642ff7e4a712cf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_12.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 363 to minute 364. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The analyzed 60-second window is 363-364 minutes. Ground truth: the first target event is a hypopnea annotation (OH) from 21780 to 21810 seconds, followed by an arousal from 21810 to 21840 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_13.json b/sleep_assessment/cases/case37/case37_13.json deleted file mode 100644 index 2bbc5e771e0b36b5208ab8577e9d1256c29c8b3d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_13.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 342.5 to minute 343.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. The analyzed 60-second window is 342.5-343.5 minutes. Ground truth: the first target event is a hypopnea annotation (MH) from 20550 to 20580 seconds, followed by an arousal from 20580 to 20610 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_14.json b/sleep_assessment/cases/case37/case37_14.json deleted file mode 100644 index 7ef1ac14d8ffa34cbb15d5812faf9160eb9b7563..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_14.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 345.5 to minute 346.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. The analyzed 60-second window is 345.5-346.5 minutes. Ground truth: the first target event is a hypopnea annotation (MH) from 20730 to 20760 seconds, followed by an arousal from 20760 to 20790 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_15.json b/sleep_assessment/cases/case37/case37_15.json deleted file mode 100644 index c74f0b761d40ed60e0eeefca9ab613e908c25107..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_15.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 179 to minute 180. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The analyzed 60-second window is 179-180 minutes. Ground truth: the first target event is a hypopnea annotation (OH) from 10740 to 10770 seconds, followed by an arousal from 10770 to 10800 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_16.json b/sleep_assessment/cases/case37/case37_16.json deleted file mode 100644 index ed1a9066a9b0278145a1da25a59288b09f7c9096..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_16.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 237 to minute 238. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The analyzed 60-second window is 237-238 minutes. Ground truth: the first target event is a apnea annotation (CA) from 14220 to 14250 seconds, followed by an arousal from 14250 to 14280 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_17.json b/sleep_assessment/cases/case37/case37_17.json deleted file mode 100644 index 147c6f987e2a48049deadf5a19a09e8dba968c32..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_17.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 80 to minute 81. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The analyzed 60-second window is 80-81 minutes. Ground truth: the first target event is a hypopnea annotation (MH) from 4800 to 4830 seconds, followed by an arousal from 4830 to 4860 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_18.json b/sleep_assessment/cases/case37/case37_18.json deleted file mode 100644 index 7e721e963a96a03499837004b1ef9f6db75c07b9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_18.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 130 to minute 131. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The analyzed 60-second window is 130-131 minutes. Ground truth: the first target event is a hypopnea annotation (MH) from 7800 to 7830 seconds, followed by an arousal from 7830 to 7860 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_19.json b/sleep_assessment/cases/case37/case37_19.json deleted file mode 100644 index b7e8a9e20a21a15422ae1530903c81650ccc040e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_19.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 267.5 to minute 268.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The analyzed 60-second window is 267.5-268.5 minutes. Ground truth: the first target event is a hypopnea annotation (OH) from 16050 to 16080 seconds, followed by an arousal from 16080 to 16110 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_20.json b/sleep_assessment/cases/case37/case37_20.json deleted file mode 100644 index 565b4a274ed72b1daa7c43ab1291fd0a37c47881..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_20.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 315.5 to minute 316.5. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The analyzed 60-second window is 315.5-316.5 minutes. Ground truth: the first target event is a hypopnea annotation (OH) from 18930 to 18960 seconds, followed by an arousal from 18960 to 18990 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_21.json b/sleep_assessment/cases/case37/case37_21.json deleted file mode 100644 index 90836508791abf02a71dba3c17a3167a43047fab..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_21.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 304.753333 to minute 305.753333. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The analyzed 60-second window is 304.753333-305.753333 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 18296.8 to 18324.9 seconds, followed by an arousal from 18325.1 to 18345.2 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_22.json b/sleep_assessment/cases/case37/case37_22.json deleted file mode 100644 index a17c64cc6063d6f99988dd018154d08bd0e3125c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_22.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 419.753333 to minute 420.753333. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The analyzed 60-second window is 419.753333-420.753333 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 25208.2 to 25221.4 seconds, followed by an arousal from 25222.8 to 25245.2 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_23.json b/sleep_assessment/cases/case37/case37_23.json deleted file mode 100644 index c9072b319edbcdf5d19690e8ffd95484fbf91bea..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_23.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 150.731667 to minute 151.731667. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The analyzed 60-second window is 150.731667-151.731667 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 9066 to 9095 seconds, followed by an arousal from 9096.5 to 9103.9 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_24.json b/sleep_assessment/cases/case37/case37_24.json deleted file mode 100644 index 69301c6e21fd9dbad297409308ec3627a136e0bb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_24.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 439.416667 to minute 440.416667. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The analyzed 60-second window is 439.416667-440.416667 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 26382 to 26397 seconds, followed by an arousal from 26399.7 to 26410.4 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_25.json b/sleep_assessment/cases/case37/case37_25.json deleted file mode 100644 index ee3d3acd4c115e96cc8fb172eed937878c916d0e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_25.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 17.951667 to minute 18.951667. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The analyzed 60-second window is 17.951667-18.951667 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 1080 to 1111 seconds, followed by an arousal from 1111 to 1128.8 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_26.json b/sleep_assessment/cases/case37/case37_26.json deleted file mode 100644 index e47e14682da7b6ccaba3da14252fa8a18eaff5ea..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_26.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 49.585 to minute 50.585. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The analyzed 60-second window is 49.585-50.585 minutes. Ground truth: the first target event is a apnea annotation (Obstructive Apnea) from 2977 to 3024 seconds, followed by an arousal from 3025.5 to 3035.1 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_27.json b/sleep_assessment/cases/case37/case37_27.json deleted file mode 100644 index f14deb265582b3b9f774c043d0f81b94b1cc3f52..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_27.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 76.83 to minute 77.83. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The analyzed 60-second window is 76.83-77.83 minutes. Ground truth: the first target event is a apnea annotation (Obstructive Apnea) from 4626 to 4650 seconds, followed by an arousal from 4652.5 to 4669.8 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_28.json b/sleep_assessment/cases/case37/case37_28.json deleted file mode 100644 index 9ad258906c6e3b43316c9949d35f6b9c2202b17b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_28.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 197.353333 to minute 198.353333. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The analyzed 60-second window is 197.353333-198.353333 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 11845 to 11866 seconds, followed by an arousal from 11866.5 to 11877.4 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_29.json b/sleep_assessment/cases/case37/case37_29.json deleted file mode 100644 index ba22dc78c959eec61f2c60df5e851276b806d014..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_29.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 231.753333 to minute 232.753333. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The analyzed 60-second window is 231.753333-232.753333 minutes. Ground truth: the first target event is a apnea annotation (Obstructive Apnea) from 13912.7 to 13944 seconds, followed by an arousal from 13944.1 to 13965.2 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_30.json b/sleep_assessment/cases/case37/case37_30.json deleted file mode 100644 index f254f506db1dd1d448d87a77f576273699ebfe46..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_30.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 146.128333 to minute 147.128333. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The analyzed 60-second window is 146.128333-147.128333 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 8804.5 to 8817.7 seconds, followed by an arousal from 8821.4 to 8827.7 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_31.json b/sleep_assessment/cases/case37/case37_31.json deleted file mode 100644 index 7838448a73c430eb1d576cade7d1dbadefea23e1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_31.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 152.875 to minute 153.875. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The analyzed 60-second window is 152.875-153.875 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 9214.1 to 9224.5 seconds, followed by an arousal from 9228.3 to 9232.5 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_32.json b/sleep_assessment/cases/case37/case37_32.json deleted file mode 100644 index 17fcd356360fffcdc8eb8f8c5e1b7d00631fe810..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_32.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 213.486667 to minute 214.486667. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The analyzed 60-second window is 213.486667-214.486667 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 12842.2 to 12857 seconds, followed by an arousal from 12863.9 to 12869.2 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_33.json b/sleep_assessment/cases/case37/case37_33.json deleted file mode 100644 index e15e1f4cedea35251c89c7daf4fad36d33ad1f36..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_33.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 145.213333 to minute 146.213333. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The analyzed 60-second window is 145.213333-146.213333 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 8744.8 to 8766.6 seconds, followed by an arousal from 8766.6 to 8772.8 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_34.json b/sleep_assessment/cases/case37/case37_34.json deleted file mode 100644 index 562e141172a41334896e3846bdc434a721941ebe..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_34.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 61.52 to minute 62.52. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The analyzed 60-second window is 61.52-62.52 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 3714.3 to 3730.1 seconds, followed by an arousal from 3730.2 to 3741.6 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_35.json b/sleep_assessment/cases/case37/case37_35.json deleted file mode 100644 index a0f733ee77c9df6a14660c677ab4599bc087e393..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_35.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 297.263333 to minute 298.263333. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The analyzed 60-second window is 297.263333-298.263333 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 17857 to 17881 seconds, followed by an arousal from 17881.9 to 17895.8 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_36.json b/sleep_assessment/cases/case37/case37_36.json deleted file mode 100644 index 44165403a01e5096cbb30231723d1786e6ece8e5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_36.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 224.8 to minute 225.8. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The analyzed 60-second window is 224.8-225.8 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 13508 to 13530 seconds, followed by an arousal from 13531 to 13548 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_37.json b/sleep_assessment/cases/case37/case37_37.json deleted file mode 100644 index e5cc8fb33be406a881dd86800cf63a4c8d38c37d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_37.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 288.38 to minute 289.38. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The analyzed 60-second window is 288.38-289.38 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 17332.1 to 17352.6 seconds, followed by an arousal from 17352.8 to 17362.8 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_38.json b/sleep_assessment/cases/case37/case37_38.json deleted file mode 100644 index 4530f0c41ad61b22c1f00c01ab07b2c392694a33..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_38.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 389.641667 to minute 390.641667. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The analyzed 60-second window is 389.641667-390.641667 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 23382.6 to 23397.1 seconds, followed by an arousal from 23398.2 to 23415.2 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_39.json b/sleep_assessment/cases/case37/case37_39.json deleted file mode 100644 index cc4efc09e387ac6b0a4d842960f8cb26bacfd6bf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_39.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 397.076667 to minute 398.076667. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The analyzed 60-second window is 397.076667-398.076667 minutes. Ground truth: the first target event is a apnea annotation (Central Apnea) from 23825.3 to 23845.5 seconds, followed by an arousal from 23850.7 to 23857 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as apnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case37/case37_40.json b/sleep_assessment/cases/case37/case37_40.json deleted file mode 100644 index 0ef54a8423005c589c1549ff4eb19349541a0771..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case37/case37_40.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Given the sleep file and paired sleep-stage label file, analyze the 60-second segment from minute 81.556667 to minute 82.556667. Identify the sleep events in chronological order, distinguishing apnea, hypopnea, arousal, and no event. Determine whether an apnea or hypopnea is followed by an arousal, then briefly analyze the likely impact of this event sequence on sleep continuity, sleep fragmentation, and restorative sleep. Describe an association supported by the temporal sequence rather than claiming that timing alone proves causality. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-event analysis reports.\n\n### TASK\nPreserve the complete substantive response for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or text outside the JSON object.\n3. The key must be EXACTLY \"full_text\".\n4. The value must be the complete report text as a string or null.\n5. If no substantive report is present, return null.\n\n### OUTPUT TEMPLATE\n{\"full_text\": }", - "metrics": [ - { - "metric_id": "respiratory_event_arousal_impact_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 100, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The analyzed 60-second window is 81.556667-82.556667 minutes. Ground truth: the first target event is a hypopnea annotation (Hypopnea) from 4922.2 to 4940.5 seconds, followed by an arousal from 4942.4 to 4953.4 seconds. Score the response additively using exactly these three criteria. Award 0.3 if it correctly identifies the first event as hypopnea; a correct specific subtype or source label is acceptable when it maps to that class. Award 0.3 if it correctly identifies a subsequent arousal and preserves the respiratory-event-then-arousal order; do not award this criterion if the order is reversed. Award 0.4 if it reasonably explains that this respiratory-event/arousal sequence is associated with sleep interruption, reduced sleep continuity, sleep fragmentation, or impaired restorative sleep. Do not require a definitive causal claim; wording such as associated with, may contribute to, or likely contributes to is correct. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_01.json b/sleep_assessment/cases/case38/case38_01.json deleted file mode 100644 index 1412b54954b7f74d065d5158dc7dd1b45d05af81..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_01.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 1.5584415584415585, - "tolerance": 0.15584415584415587 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. Ground truth: N3 arousal transition count is 0, and arousal-like disruption is no. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 0 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_02.json b/sleep_assessment/cases/case38/case38_02.json deleted file mode 100644 index 8285941cbf86d0c654ccfbb1202ae6a6ef26a9d6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_02.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. Ground truth: N3 arousal transition count is 0, and arousal-like disruption is no. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 0 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_03.json b/sleep_assessment/cases/case38/case38_03.json deleted file mode 100644 index 76e478cacdd07d8431ab2dc1c606731188467ae2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_03.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 0.6936416184971098, - "tolerance": 0.06936416184971099 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 1.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. Ground truth: N3 arousal transition count is 1, and arousal-like disruption is yes. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 1 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_04.json b/sleep_assessment/cases/case38/case38_04.json deleted file mode 100644 index db94cb93743bc806ce9c4ed0d284213e3ebef126..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_04.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 1.1214953271028036, - "tolerance": 0.11214953271028037 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 1.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. Ground truth: N3 arousal transition count is 1, and arousal-like disruption is yes. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 1 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_05.json b/sleep_assessment/cases/case38/case38_05.json deleted file mode 100644 index 0ab7adafbfc33a4d57c695330afa3854f54c3ab9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_05.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. Ground truth: N3 arousal transition count is 0, and arousal-like disruption is no. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 0 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_06.json b/sleep_assessment/cases/case38/case38_06.json deleted file mode 100644 index c0a7785b4fca21d603b761c4721c5b0731d63bdb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_06.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 5.793103448275862, - "tolerance": 0.5793103448275863 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 5.0, - "tolerance": 1.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. Ground truth: N3 arousal transition count is 5, and arousal-like disruption is yes. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 5 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_07.json b/sleep_assessment/cases/case38/case38_07.json deleted file mode 100644 index d5378ff44b1ef047d7cb32de734426bc242e97b9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_07.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 17.704918032786885, - "tolerance": 1.7704918032786887 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 8.0, - "tolerance": 1.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. Ground truth: N3 arousal transition count is 8, and arousal-like disruption is yes. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 8 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_08.json b/sleep_assessment/cases/case38/case38_08.json deleted file mode 100644 index 239fc2392bbec9d2fdfeb7e2aac83980510fed14..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_08.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 11.335504885993487, - "tolerance": 1.1335504885993488 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 8.0, - "tolerance": 1.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. Ground truth: N3 arousal transition count is 8, and arousal-like disruption is yes. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 8 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_09.json b/sleep_assessment/cases/case38/case38_09.json deleted file mode 100644 index 7aa2e34138ecd1b3c5cc1c98edf4ab93462e17d5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_09.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 2.2857142857142856, - "tolerance": 0.22857142857142856 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 4.0, - "tolerance": 1.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. Ground truth: N3 arousal transition count is 4, and arousal-like disruption is yes. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 4 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case38/case38_10.json b/sleep_assessment/cases/case38/case38_10.json deleted file mode 100644 index 4b1c339e63f7c8a32adb335a509420ab49d356c1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case38/case38_10.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, analyze deep sleep disruption across the whole night. Use the sleep labels with 0=W, 1=N1, 2=N2, 3=N3, and 4=R/REM. Count arousal events whose start time falls in an N3 epoch. Compute N3-ARI as the number of N3 arousal events divided by N3 sleep time in hours. Also count how many N3 arousal events are followed by a lighter or wake stage in the next 30-second epoch, where W, N1, N2, or R/REM all count as non-N3 transitions. Report the N3-ARI, the N3 arousal transition count, and judge whether nighttime deep sleep shows arousal-like disruption. Arousal-like disruption is present when the transition count is greater than 0." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final N3 arousal index, N3 arousal transition count, and preserve the report text for semantic judging.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"n3_ari\", \"n3_arousal_transition_count\", and \"full_text\".\n4. \"n3_ari\" must be a FLOAT or null.\n5. \"n3_arousal_transition_count\" must be a FLOAT or null.\n6. \"full_text\" must be the complete substantive report text as a string or null.\n7. If a numeric value is missing or unclear, return null for that numeric key. If no substantive report is present, return null for \"full_text\".\n\n### OUTPUT TEMPLATE\n{\"n3_ari\": , \"n3_arousal_transition_count\": , \"full_text\": }", - "metrics": [ - { - "metric_id": "n3_arousal_index_accuracy", - "type": "numeric_check", - "target_key": "n3_ari", - "weight": 35, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "n3_arousal_transition_count_accuracy", - "type": "numeric_check", - "target_key": "n3_arousal_transition_count", - "weight": 35, - "params": { - "gt_value": 0.0, - "tolerance": 1.0 - } - }, - { - "metric_id": "n3_arousal_like_disruption_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 30, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. Ground truth: N3 arousal transition count is 0, and arousal-like disruption is no. The decision threshold is strict: disruption is present if and only if the N3 arousal transition count is greater than 0. Score additively using exactly these criteria. Award 0.4 if the response correctly states whether arousal-like disruption is present. Award 0.3 if it correctly uses or is consistent with the transition-count threshold; for this sample that means recognizing 0 transition event(s) relative to the >0 rule. Award 0.3 if the clinical interpretation is directionally correct: for a positive case, N3 arousal followed by W/N1/N2/R indicates disrupted deep sleep continuity, sleep fragmentation, or impaired restorative sleep; for a negative case, there is no observed N3 arousal followed by a non-N3 stage under this rule. Do not require a definitive causal claim; associated with, suggests, or may contribute to is acceptable. The final score must be the sum of the earned criteria and therefore one of 0.0, 0.3, 0.4, 0.6, 0.7, or 1.0. Output only one valid JSON object with exactly one key: {\"score\": }. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case39/case39_01.json b/sleep_assessment/cases/case39/case39_01.json deleted file mode 100644 index e4e9e0415a368e0a225d69109afb351315c75660..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_01.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 31.0, - "tolerance": 6.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_02.json b/sleep_assessment/cases/case39/case39_02.json deleted file mode 100644 index db877eec0f98808e989d516a53303b8ef4371e7c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_02.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 3.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N1" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_03.json b/sleep_assessment/cases/case39/case39_03.json deleted file mode 100644 index 233caec930173bade017e5c84a049743ff220a7e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_03.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 1.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N1" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_04.json b/sleep_assessment/cases/case39/case39_04.json deleted file mode 100644 index 1c94a7216e59a924e145e6c18860ca43be51e909..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_04.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_04.edf", - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 1.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_05.json b/sleep_assessment/cases/case39/case39_05.json deleted file mode 100644 index a2bd51c2390e653fe9c9eeafedf73225066ec70c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_05.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 5.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_06.json b/sleep_assessment/cases/case39/case39_06.json deleted file mode 100644 index 541af8b96777f68339db0e41036b9997cf670dd7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_06.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 12.0, - "tolerance": 4.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_07.json b/sleep_assessment/cases/case39/case39_07.json deleted file mode 100644 index 5737857a7a5615df89d60d03f4c0f0fc383e6f41..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_07.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 32.0, - "tolerance": 6.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_08.json b/sleep_assessment/cases/case39/case39_08.json deleted file mode 100644 index 5284a3d1f85c05d9b86127d2e8a5974c050ad2bd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_08.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 20.0, - "tolerance": 4.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_09.json b/sleep_assessment/cases/case39/case39_09.json deleted file mode 100644 index bfddd1a267f3755f1ba65de112a13488e8b8813d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_09.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 21.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_10.json b/sleep_assessment/cases/case39/case39_10.json deleted file mode 100644 index c917a1d02f022dbf5a1dff9d41d4d88b055583a3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_10.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for SHHS1: THOR RES = thoracic respiratory effort; ABDO RES = abdominal respiratory effort; NEW AIR = nasal/oral airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 15.0, - "tolerance": 4.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_11.json b/sleep_assessment/cases/case39/case39_11.json deleted file mode 100644 index 5a0913b096b8fad9729661f71d3ded4ac96e2d96..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_11.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 4.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_12.json b/sleep_assessment/cases/case39/case39_12.json deleted file mode 100644 index 8930dc43be51e6f340a1a2e8665780221833bb01..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_12.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 23.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_13.json b/sleep_assessment/cases/case39/case39_13.json deleted file mode 100644 index efd2d08815dd3861e25687e158698d93fcfe105c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_13.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "NULL" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_14.json b/sleep_assessment/cases/case39/case39_14.json deleted file mode 100644 index 10b0a6e37ff89401dcd0a27b8600e113c87b7ec3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_14.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 25.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case39/case39_15.json b/sleep_assessment/cases/case39/case39_15.json deleted file mode 100644 index 0ae1b1bb3961e624cedf8e08ba16f889d3aeb1b5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case39/case39_15.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 2, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Given the sleep EEG/PSG file and paired sleep-stage label file, assess whole-night respiratory event clustering. Divide the recording into fixed, non-overlapping 5-minute windows starting at 0 minutes: 0-5, 5-10, 10-15, and so on. Use only complete 5-minute windows. Count apnea and hypopnea candidate events by their event onset time; an event belongs to exactly one half-open window [start, end). A cluster window is a 5-minute window with at least 3 apnea/hypopnea candidate events. For each cluster window, determine the dominant sleep stage from the ten 30-second sleep-stage labels in that window. If the dominant stage is tied, use the sleep stages at the candidate event onset epochs within the window to break the tie; if still tied, use W, N1, N2, N3, R order. Report the total number of cluster windows and the dominant stage across all cluster windows. If no cluster windows occur, report dominant stage as NULL. Allowed stage outputs are W, N1, N2, N3, R, and NULL. Respiratory channels for Physionet2018: ABD = abdominal respiratory effort; CHEST = thoracic respiratory effort; AIRFLOW = airflow; SaO2 = oximetry." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the total respiratory cluster-window count and dominant cluster sleep stage.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"cluster_window_count\" and \"dominant_cluster_stage\".\n4. \"cluster_window_count\" must be a FLOAT or null.\n5. \"dominant_cluster_stage\" must be one of \"W\", \"N1\", \"N2\", \"N3\", \"R\", \"NULL\", or null.\n6. If there are no respiratory cluster windows, normalize the stage to \"NULL\".\n7. If a value is missing or unclear, return null for that key.\n\n### OUTPUT TEMPLATE\n{\"cluster_window_count\": , \"dominant_cluster_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|\"NULL\"|null>}", - "metrics": [ - { - "metric_id": "respiratory_cluster_window_count_accuracy", - "type": "numeric_check", - "target_key": "cluster_window_count", - "weight": 60, - "params": { - "gt_value": 42.0, - "tolerance": 6.0 - } - }, - { - "metric_id": "respiratory_cluster_dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_cluster_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} \ No newline at end of file diff --git a/sleep_assessment/cases/case4/case4_01.json b/sleep_assessment/cases/case4/case4_01.json deleted file mode 100644 index d90ff7eedda29fa72d5d5d2ff02cb13d72c6358f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_01.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 126.5, - "tolerance": 6.325 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 27, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_02.json b/sleep_assessment/cases/case4/case4_02.json deleted file mode 100644 index 26098a718a854fd6c92d84c0177359b84dbf16fc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_02.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 67.5, - "tolerance": 3.375 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 30, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_03.json b/sleep_assessment/cases/case4/case4_03.json deleted file mode 100644 index 66d21af5fb38b1bf972d978995fc863243c5a9af..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_03.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 62.5, - "tolerance": 3.125 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 49, - "tolerance": 2 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_04.json b/sleep_assessment/cases/case4/case4_04.json deleted file mode 100644 index 93b47abfc9638fcb38d91e57cdee1a48130510c0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_04.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 13.0, - "tolerance": 0.65 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 22, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_05.json b/sleep_assessment/cases/case4/case4_05.json deleted file mode 100644 index 5e928f8ac3b622452384b8f511da4deb8c26c716..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_05.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 47.5, - "tolerance": 2.375 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 20, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_06.json b/sleep_assessment/cases/case4/case4_06.json deleted file mode 100644 index 5dff3457c273c5568e10467a0cd120c9c7a2d608..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_06.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 66.5, - "tolerance": 3.325 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 12, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_07.json b/sleep_assessment/cases/case4/case4_07.json deleted file mode 100644 index 82caa58a61c2a01a8c9a0c18ebdcfea965d9c02c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_07.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 37.0, - "tolerance": 1.85 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 20, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_08.json b/sleep_assessment/cases/case4/case4_08.json deleted file mode 100644 index 92c226c906bdcfde0bcc923cfffd089277f0bc9a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_08.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 195.5, - "tolerance": 9.775 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 33, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_09.json b/sleep_assessment/cases/case4/case4_09.json deleted file mode 100644 index ae40268e67b63974b0f6bf64ea1189b63c4473d5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_09.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 33.5, - "tolerance": 1.675 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 28, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_10.json b/sleep_assessment/cases/case4/case4_10.json deleted file mode 100644 index 9b8cda3d8e9a2eba45750092e46264d1c36e5a72..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_10.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 76.5, - "tolerance": 3.825 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 44, - "tolerance": 2 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_11.json b/sleep_assessment/cases/case4/case4_11.json deleted file mode 100644 index 13970e1a6a78a9700d2d7e7c624ee923c8778437..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_11.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 65.0, - "tolerance": 3.25 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 46, - "tolerance": 2 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_12.json b/sleep_assessment/cases/case4/case4_12.json deleted file mode 100644 index 8e8ee87ce2273eb294eef47811a3f8e93e55b751..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_12.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 43.0, - "tolerance": 2.15 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 14, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_13.json b/sleep_assessment/cases/case4/case4_13.json deleted file mode 100644 index 651961cadcb5d946da3743c04c0e20279ce940d9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_13.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 73.0, - "tolerance": 3.6500000000000004 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 31, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_14.json b/sleep_assessment/cases/case4/case4_14.json deleted file mode 100644 index b6381e2880dbfdc096dab091e1f19850ea827270..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_14.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 43.5, - "tolerance": 2.1750000000000003 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 23, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_15.json b/sleep_assessment/cases/case4/case4_15.json deleted file mode 100644 index 8407cbb4dd6563b432ac4ed7acdd091384212303..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_15.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 100.0, - "tolerance": 5.0 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 35, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_16.json b/sleep_assessment/cases/case4/case4_16.json deleted file mode 100644 index 2cc4545dbd539063b887e8ad0b5b530b43829c7e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_16.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 30.5, - "tolerance": 1.5250000000000001 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 22, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_17.json b/sleep_assessment/cases/case4/case4_17.json deleted file mode 100644 index 10e6deb7cb514337843354317d0038389e3172b5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_17.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 37.5, - "tolerance": 1.875 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 31, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_18.json b/sleep_assessment/cases/case4/case4_18.json deleted file mode 100644 index 91c1d1d75f5e6540313b001bf4efbea6037521d8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_18.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 19.0, - "tolerance": 0.9500000000000001 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 19, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_19.json b/sleep_assessment/cases/case4/case4_19.json deleted file mode 100644 index 914a9ed923e6bc05a6b59fbac48035cf4d62f666..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_19.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 3.5, - "tolerance": 0.5 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 7, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_20.json b/sleep_assessment/cases/case4/case4_20.json deleted file mode 100644 index 36c530f0df5d8ab8a1674ed62919a1bf2b3e22f8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_20.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 19.5, - "tolerance": 0.9750000000000001 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 23, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_21.json b/sleep_assessment/cases/case4/case4_21.json deleted file mode 100644 index b72b7f8afa232806b3ab94b3790c264ffc3752d5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_21.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 151.5, - "tolerance": 7.575 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 27, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_22.json b/sleep_assessment/cases/case4/case4_22.json deleted file mode 100644 index 72abb7dac0a0f9fbe0025a1346607eefa530d469..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_22.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 148.0, - "tolerance": 7.4 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 31, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_23.json b/sleep_assessment/cases/case4/case4_23.json deleted file mode 100644 index 75ad85b5409176c22cf181f0a52321ff16234eec..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_23.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 168.5, - "tolerance": 8.425 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 11, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_24.json b/sleep_assessment/cases/case4/case4_24.json deleted file mode 100644 index 1ab15c16d149299833a7f2a037e6f2160c7182b8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_24.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 40.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 21, - "tolerance": 1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case4/case4_25.json b/sleep_assessment/cases/case4/case4_25.json deleted file mode 100644 index 7b45ecbede8b069ded756bc47e18b68dba357a08..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case4/case4_25.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two sleep continuity metrics for the recording: Wake After Sleep Onset (WASO) in minutes, and the number of awakenings after sleep onset. Define the analysis interval from the first sleep epoch to the last sleep epoch, and count awakenings as contiguous Wake segments within that interval. Please clearly report both final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two values from the agent's report:\n1) Wake After Sleep Onset (WASO) in minutes\n2) the number of awakenings after sleep onset\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"waso_min\" and \"awakening_count\".\n5. \"waso_min\" must be a FLOAT in minutes or null.\n6. \"awakening_count\" must be an INTEGER or null.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"waso_min\": , \"awakening_count\": }", - "metrics": [ - { - "metric_id": "waso_accuracy", - "type": "numeric_check", - "target_key": "waso_min", - "weight": 50, - "params": { - "gt_value": 28.5, - "tolerance": 1.425 - } - }, - { - "metric_id": "awakening_count_accuracy", - "type": "numeric_check", - "target_key": "awakening_count", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_01.json b/sleep_assessment/cases/case40/case40_01.json deleted file mode 100644 index 2174a53758492d1d41b6564d3253579b7f9b8d35..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_01.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. ISRUC channels: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 2.727272727272727, - "tolerance": 0.13636363636363635 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 2.727272727272727, - "tolerance": 0.13636363636363635 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_02.json b/sleep_assessment/cases/case40/case40_02.json deleted file mode 100644 index 45a0d0384c7499c61c610010c93075a6778c37c9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_02.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. ISRUC channels: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 0.3337969401947149, - "tolerance": 0.016689847009735744 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_03.json b/sleep_assessment/cases/case40/case40_03.json deleted file mode 100644 index cac0577e7942573d01871d40583b692bbd33b4b2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_03.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. ISRUC channels: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_04.json b/sleep_assessment/cases/case40/case40_04.json deleted file mode 100644 index ce0c03cf9f89fc6e2ec763f691bf65542e70f9db..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_04.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. ISRUC channels: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 1.8652849740932642, - "tolerance": 0.09326424870466321 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 0.20725388601036268, - "tolerance": 0.010362694300518135 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_05.json b/sleep_assessment/cases/case40/case40_05.json deleted file mode 100644 index 4c19c6f8249a03eaecad430b0e8c4dc5e6e9ad7e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_05.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. SHHS1 channels: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 1.4380825565912116, - "tolerance": 0.07190412782956059 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 0.6391478029294274, - "tolerance": 0.03195739014647137 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_06.json b/sleep_assessment/cases/case40/case40_06.json deleted file mode 100644 index e30cd06bf39e39e9919feec18f1c21e5aaae7cd8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_06.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_06", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. SHHS1 channels: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 7.582417582417583, - "tolerance": 0.37912087912087916 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 6.593406593406594, - "tolerance": 0.3296703296703297 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_07.json b/sleep_assessment/cases/case40/case40_07.json deleted file mode 100644 index 772393231bcc60634952e486fc843abe4161ba1a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_07.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_07", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. SHHS1 channels: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 5.020920502092051, - "tolerance": 0.25104602510460255 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 2.5104602510460254, - "tolerance": 0.12552301255230128 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_08.json b/sleep_assessment/cases/case40/case40_08.json deleted file mode 100644 index 8911a43bcde35152616c614ad5f6e5202c7683a9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_08.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_08", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. SHHS1 channels: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 0.9966777408637874, - "tolerance": 0.04983388704318937 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 0.5980066445182725, - "tolerance": 0.029900332225913623 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_09.json b/sleep_assessment/cases/case40/case40_09.json deleted file mode 100644 index ef5ce910f06d24095057264105c8f7e2db9e607b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_09.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_09", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. SHHS1 channels: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 1.9459459459459458, - "tolerance": 0.0972972972972973 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 1.6216216216216215, - "tolerance": 0.08108108108108109 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_10.json b/sleep_assessment/cases/case40/case40_10.json deleted file mode 100644 index 2664e5226fa40f44b882ebf3f3413283061f91cd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_10.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_10", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. Physionet2018 channels: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 8.535564853556485, - "tolerance": 0.42677824267782427 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 2.510460251046025, - "tolerance": 0.12552301255230125 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_11.json b/sleep_assessment/cases/case40/case40_11.json deleted file mode 100644 index 0a738ef2fb26b0601066a496b6fe76c6aa4b294e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_11.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_11", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. Physionet2018 channels: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 9.391304347826086, - "tolerance": 0.4695652173913043 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 8.869565217391305, - "tolerance": 0.44347826086956527 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_12.json b/sleep_assessment/cases/case40/case40_12.json deleted file mode 100644 index e044db24f0b130ce98174f5a9f05ec4c0a3a1d43..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_12.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_12", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. Physionet2018 channels: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 1.0619469026548674, - "tolerance": 0.05309734513274337 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 0.5309734513274337, - "tolerance": 0.026548672566371685 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_13.json b/sleep_assessment/cases/case40/case40_13.json deleted file mode 100644 index f89ccd650188bb0baf18590c9a6f43e713bd946f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_13.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_13", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. Physionet2018 channels: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 13.211009174311927, - "tolerance": 0.6605504587155964 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 10.851900393184797, - "tolerance": 0.5425950196592398 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case40/case40_14.json b/sleep_assessment/cases/case40/case40_14.json deleted file mode 100644 index 17a94b6323d0cb895321da491263132db69927cd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case40/case40_14.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case40_14", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Use the sleep file and paired sleep-stage labels to calculate sleep-period ODI3 and ODI4. Physionet2018 channels: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow. Report final ODI3 and ODI4 in events/hour." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the whole-night sleep oxygen desaturation indices ODI3 and ODI4.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. The keys must be EXACTLY \"odi3\" and \"odi4\".\n4. Both values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"odi3\": , \"odi4\": }", - "metrics": [ - { - "metric_id": "oxygen_desaturation_index_3_accuracy", - "type": "numeric_check", - "target_key": "odi3", - "weight": 50, - "params": { - "gt_value": 10.266666666666667, - "tolerance": 0.5133333333333334 - } - }, - { - "metric_id": "oxygen_desaturation_index_4_accuracy", - "type": "numeric_check", - "target_key": "odi4", - "weight": 50, - "params": { - "gt_value": 8.933333333333334, - "tolerance": 0.4466666666666667 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_01.json b/sleep_assessment/cases/case41/case41_01.json deleted file mode 100644 index 2f8201e1257b1f14bc28933627a1d884070114c5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_01.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. ISRUC: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 95.31661652458806, - "tolerance": 4.765830826229403 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 96.99387057428399, - "tolerance": 4.8496935287142 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 80.18999154305723, - "tolerance": 4.009499577152861 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_02.json b/sleep_assessment/cases/case41/case41_02.json deleted file mode 100644 index ce859a90851b0bc2c729200c230d3c86dc96c4cc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_02.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. ISRUC: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 96.35141244472393, - "tolerance": 4.817570622236197 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 96.3768491908515, - "tolerance": 4.818842459542576 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 90.3370649259306, - "tolerance": 4.51685324629653 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_03.json b/sleep_assessment/cases/case41/case41_03.json deleted file mode 100644 index 54f01b11932c956112476c01515edfaa181bdd01..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_03.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. ISRUC: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 99.68644538175248, - "tolerance": 4.984322269087624 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 99.7551652699975, - "tolerance": 4.987758263499876 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 97.4486915388724, - "tolerance": 4.87243457694362 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_04.json b/sleep_assessment/cases/case41/case41_04.json deleted file mode 100644 index b5036c3783751566f56c1d4d408bdcd17c0e8ecf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_04.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. ISRUC: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 98.01404911485601, - "tolerance": 4.900702455742801 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 99.07532700594251, - "tolerance": 4.953766350297126 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 92.3925677386931, - "tolerance": 4.6196283869346555 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_05.json b/sleep_assessment/cases/case41/case41_05.json deleted file mode 100644 index 315e8efeedd3de8dd3118bee21505b695d62ba93..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_05.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 93.06258265255414, - "tolerance": 4.653129132627707 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 93.52465920576844, - "tolerance": 4.676232960288423 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 85.30551749480685, - "tolerance": 4.265275874740342 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_06.json b/sleep_assessment/cases/case41/case41_06.json deleted file mode 100644 index a7ce9ee14450f73c6146f67daadf314c73e480cd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_06.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_06", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 94.5460224126736, - "tolerance": 4.72730112063368 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 94.22105120816956, - "tolerance": 4.711052560408478 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 83.20339248179185, - "tolerance": 4.160169624089593 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_07.json b/sleep_assessment/cases/case41/case41_07.json deleted file mode 100644 index e2ee691e485388c1a5736b712abbfaeb66f5de0c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_07.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_07", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 95.62549723032568, - "tolerance": 4.781274861516284 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 96.08619241225712, - "tolerance": 4.804309620612856 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 83.95213313180965, - "tolerance": 4.197606656590483 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_08.json b/sleep_assessment/cases/case41/case41_08.json deleted file mode 100644 index 96d7198f24f4f6c0cd61ef1c520242bb9a44f16b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_08.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_08", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 96.11838338081978, - "tolerance": 4.8059191690409895 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 96.28846366637599, - "tolerance": 4.8144231833188 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 92.28865598094711, - "tolerance": 4.614432799047356 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_09.json b/sleep_assessment/cases/case41/case41_09.json deleted file mode 100644 index ac5fce2c7485c99797f9499b7b6e73e14521528e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_09.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_09", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 94.84243082865771, - "tolerance": 4.742121541432886 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 95.96471019336792, - "tolerance": 4.798235509668396 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 84.70099572079334, - "tolerance": 4.235049786039667 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_10.json b/sleep_assessment/cases/case41/case41_10.json deleted file mode 100644 index d8e53c9cb64edb5f6f050d75c7b10974d8b79a2d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_10.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_10", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 95.22697915185633, - "tolerance": 4.7613489575928165 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 96.37645633826935, - "tolerance": 4.8188228169134675 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 89.6039031990112, - "tolerance": 4.4801951599505605 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_11.json b/sleep_assessment/cases/case41/case41_11.json deleted file mode 100644 index 5e45617f60372ab77b92ce118d4cda69b57dd7e5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_11.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_11", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 90.44955375402417, - "tolerance": 4.522477687701208 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 92.04055215891857, - "tolerance": 4.602027607945929 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 71.7834291950438, - "tolerance": 3.58917145975219 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_12.json b/sleep_assessment/cases/case41/case41_12.json deleted file mode 100644 index 2401ba8babd4ca3e1a5817debc71e560e26093e6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_12.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_12", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 94.27980709415505, - "tolerance": 4.713990354707753 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 95.4768836436887, - "tolerance": 4.773844182184435 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 91.58421359923094, - "tolerance": 4.579210679961547 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_13.json b/sleep_assessment/cases/case41/case41_13.json deleted file mode 100644 index 2a294a88ce6428fb71dc1b509b24342fec963f9e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_13.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_13", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 94.13502105325266, - "tolerance": 4.706751052662633 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 94.45512625125403, - "tolerance": 4.7227563125627015 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 77.723536500412, - "tolerance": 3.8861768250206 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case41/case41_14.json b/sleep_assessment/cases/case41/case41_14.json deleted file mode 100644 index 60649c1f390bf21d1265387927e69db96c0d6320..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case41/case41_14.json +++ /dev/null @@ -1,48 +0,0 @@ -{ - "meta_info": { - "case_id": "case41_14", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds. Treat N1/N2/N3/R as sleep and W as wake. Report percentages. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract sleep mean SpO2, wake mean SpO2, and sleep minimum SpO2.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"sleep_mean_spo2\", \"wake_mean_spo2\", and \"sleep_min_spo2\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"sleep_mean_spo2\": , \"wake_mean_spo2\": , \"sleep_min_spo2\": }", - "metrics": [ - { - "metric_id": "sleep_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_mean_spo2", - "weight": 33, - "params": { - "gt_value": 91.31518765601054, - "tolerance": 4.565759382800527 - } - }, - { - "metric_id": "wake_mean_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "wake_mean_spo2", - "weight": 33, - "params": { - "gt_value": 95.84114814688597, - "tolerance": 4.7920574073442985 - } - }, - { - "metric_id": "sleep_min_oxygen_saturation_accuracy", - "type": "numeric_check", - "target_key": "sleep_min_spo2", - "weight": 34, - "params": { - "gt_value": 65.84181620123906, - "tolerance": 3.292090810061953 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_01.json b/sleep_assessment/cases/case42/case42_01.json deleted file mode 100644 index 4cb9e18b161d2c4e17076a8e81d81578584aac8f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_01.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_01.edf", - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. ISRUC: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 18.183333333333334, - "tolerance": 0.9091666666666667 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_02.json b/sleep_assessment/cases/case42/case42_02.json deleted file mode 100644 index dfbe9e3b7b262593d879759f70031b3da994b000..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_02.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_02.edf", - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. ISRUC: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_03.json b/sleep_assessment/cases/case42/case42_03.json deleted file mode 100644 index d67ae1123e660c44945440d399ceb7f3b5a4c3f5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_03.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_03.edf", - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. ISRUC: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_04.json b/sleep_assessment/cases/case42/case42_04.json deleted file mode 100644 index 5b9cfb91a366d8aedf0d561802071871f235b38c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_04.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "data_path": "data/sleep/ISRUC_05.edf", - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. ISRUC: SaO2=oxygen saturation; X5=snore; X6/DC3=airflow; X7/X8=abdominal effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_05.json b/sleep_assessment/cases/case42/case42_05.json deleted file mode 100644 index 1b4dfaf70f9a5abcf579bc3fd9d0788f4edd772c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_05.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_01.edf", - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 7.866666666666666, - "tolerance": 0.3933333333333333 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_06.json b/sleep_assessment/cases/case42/case42_06.json deleted file mode 100644 index c43822a00eca2fe69cf8e9b6b7793bf49c190f28..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_06.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_06", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_02.edf", - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 2.9833333333333334, - "tolerance": 0.14916666666666667 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_07.json b/sleep_assessment/cases/case42/case42_07.json deleted file mode 100644 index aedf75cf44b13421d929ac81447b6bf5259dd311..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_07.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_07", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_03.edf", - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 0.9666666666666667, - "tolerance": 0.04833333333333334 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_08.json b/sleep_assessment/cases/case42/case42_08.json deleted file mode 100644 index 6d1d11b0faa26988cfea6c66c015ef867f3e5ac9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_08.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_08", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_04.edf", - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_09.json b/sleep_assessment/cases/case42/case42_09.json deleted file mode 100644 index a11a02182bdbabe398b0a357f6b5eb5ff5f25bdc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_09.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_09", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "data_path": "data/sleep/SHHS1_05.edf", - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. SHHS1: SaO2=oxygen saturation; THOR RES/ABDO RES=effort; NEW AIR/AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 2.75, - "tolerance": 0.1375 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_10.json b/sleep_assessment/cases/case42/case42_10.json deleted file mode 100644 index beba1e060ecd7d4d0c424feaa3eb7bf8288cdf59..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_10.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_10", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_01.edf", - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 0.15, - "tolerance": 0.0075 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_11.json b/sleep_assessment/cases/case42/case42_11.json deleted file mode 100644 index 5ec4cd94a1e5475e15461a3edbe5ff22e4e803cf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_11.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_11", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_02.edf", - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 97.08333333333333, - "tolerance": 4.854166666666667 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 2.966666666666667, - "tolerance": 0.14833333333333334 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_12.json b/sleep_assessment/cases/case42/case42_12.json deleted file mode 100644 index 4f4b440423b63e66dc6c579a55fab6cc9706bec8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_12.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_12", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_03.edf", - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.0, - "tolerance": 0.0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_13.json b/sleep_assessment/cases/case42/case42_13.json deleted file mode 100644 index 5652cd00caecbddfa71eb244c46af10480965ab9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_13.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_13", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_04.edf", - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 28.233333333333334, - "tolerance": 1.4116666666666668 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 0.3, - "tolerance": 0.015 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case42/case42_14.json b/sleep_assessment/cases/case42/case42_14.json deleted file mode 100644 index a910f7e9c672688fa5046c3d64a8afd6f82d8907..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case42/case42_14.json +++ /dev/null @@ -1,38 +0,0 @@ -{ - "meta_info": { - "case_id": "case42_14", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "data_path": "data/sleep/Physionet2018_05.edf", - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Use the sleep file and paired labels to calculate sleep-period T90 and T80 in cumulative minutes, not percentages. Convert SaO2 to 1 Hz by per-second median; accept 50-100%, remove isolated <=2-second outliers more than 4 points from a centered 9-second median, interpolate only bounded internal gaps <=30 seconds, apply a centered 9-second median, and exclude all remaining missing seconds without extrapolation. T90 counts valid N1/N2/N3/R seconds with SaO2 strictly below 90%; T80 uses strictly below 80%. Physionet2018: SaO2=oxygen saturation; ABD/CHEST=effort; AIRFLOW=airflow." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract T90 and T80 hypoxemia burden in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, units, or extra fields.\n3. Keys must be EXACTLY \"t90_min\" and \"t80_min\".\n4. Values must be FLOAT values or null.\n5. Return null for a missing or unclear value.\n\n### OUTPUT TEMPLATE\n{\"t90_min\": , \"t80_min\": }", - "metrics": [ - { - "metric_id": "t90_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t90_min", - "weight": 50, - "params": { - "gt_value": 142.18333333333334, - "tolerance": 7.109166666666667 - } - }, - { - "metric_id": "t80_duration_burden_accuracy", - "type": "numeric_check", - "target_key": "t80_min", - "weight": 50, - "params": { - "gt_value": 13.033333333333333, - "tolerance": 0.6516666666666667 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_01.json b/sleep_assessment/cases/case43/case43_01.json deleted file mode 100644 index b7d83fa69ef5150d34aa30e7b24f07cb0230d39c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_01.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q01", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: In a standard adult PSG, which EEG rhythm is most typical over the occipital region during relaxed wakefulness with eyes closed?\nA. 0.5-2 Hz slow waves\nB. 8-13 Hz alpha rhythm\nC. 4-7 Hz theta rhythm\nD. 11-16 Hz sleep spindles" - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_02.json b/sleep_assessment/cases/case43/case43_02.json deleted file mode 100644 index 5b48edb26fc55f164ab9a9e5b215f96d4a0dced5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_02.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q02", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about scoring stage N1 is correct?\nA. Sleep spindles must be present before N1 can be scored.\nB. Slow eye movements occur only in REM sleep.\nC. Chin EMG activity is usually higher than during wakefulness.\nD. Vertex sharp waves may occur, but they are not required for scoring N1." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_03.json b/sleep_assessment/cases/case43/case43_03.json deleted file mode 100644 index e4bcc9b605325e8ee96ab139e2a70220405ae5b4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_03.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q03", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which description best characterizes vertex sharp waves?\nA. They are a type of triphasic wave typically seen in hepatic encephalopathy.\nB. They usually last at least 1 second and are maximal frontally.\nC. They last less than 0.5 seconds and are most prominent over the central region.\nD. They have a frequency of 11-16 Hz and are maximal over the central region." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_04.json b/sleep_assessment/cases/case43/case43_04.json deleted file mode 100644 index c07ef128af44507e2d624109a2e07603777f6923..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_04.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q04", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which feature is essential to the AASM definition of a K-complex?\nA. A total duration of at least 0.5 seconds, with a negative sharp wave immediately followed by a positive component.\nB. Its frequency must be 12-14 Hz.\nC. It can count toward N2 only when accompanied by an arousal.\nD. It occurs only during REM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_05.json b/sleep_assessment/cases/case43/case43_05.json deleted file mode 100644 index 829bd689b9856d7530b039acaf874e7f900ae904..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_05.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q05", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: When the criteria for N3 are not met, which rule best describes the onset of N2?\nA. N2 begins whenever chin EMG activity decreases.\nB. N2 begins whenever any 4-7 Hz theta activity appears.\nC. N2 begins when any eye movement occurs in the preceding 10 seconds of the epoch.\nD. N2 begins when a K-complex not associated with an arousal or a sleep spindle occurs in the first half of the current epoch or the second half of the preceding epoch." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_06.json b/sleep_assessment/cases/case43/case43_06.json deleted file mode 100644 index 5e8880d892e4ae83d98c77fca944096bc13ae8cb..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_06.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q06", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about sleep spindles is correct?\nA. They are 11-16 Hz, most commonly 12-14 Hz, and last at least 0.5 seconds.\nB. They are 2-6 Hz and last at least 0.5 seconds.\nC. They must last at least 3 seconds to be scored.\nD. They occur only in REM sleep and are most prominent over the occipital region." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_07.json b/sleep_assessment/cases/case43/case43_07.json deleted file mode 100644 index d460c05bc456c47c63a8cde89418f78f2555d759..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_07.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q07", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which criterion most appropriately defines stage N3?\nA. Any occurrence of delta activity is sufficient for N3.\nB. In a 30-second epoch, frontal slow-wave activity occupies at least 20%, with a frequency of 0.5-2 Hz and a peak-to-peak amplitude greater than 75 microvolts.\nC. Any K-complex is sufficient for N3.\nD. A reduction in eye movements alone is sufficient for N3." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_08.json b/sleep_assessment/cases/case43/case43_08.json deleted file mode 100644 index c99f52bd70a8a335512b0d34ac0acfbf462b8b37..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_08.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q08", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which combination best describes definite REM sleep according to the AASM?\nA. High-amplitude delta activity, high chin muscle tone, and no eye movements.\nB. Sustained alpha rhythm, high chin muscle tone, and a flat EOG.\nC. Low-amplitude mixed-frequency EEG, low chin muscle tone, and rapid eye movements.\nD. Frequent spindles, increased muscle tone, and slow eye movements." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_09.json b/sleep_assessment/cases/case43/case43_09.json deleted file mode 100644 index 6672803d3bf1ea3fab6d3205fb5b72d9780e11e4..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_09.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q09", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: After definite stage R has begun, how should a subsequent epoch be scored if it contains no new rapid eye movements but still shows low-amplitude mixed-frequency EEG and low chin muscle tone, contains no K-complexes or spindles, and has no arousal or other stage-R termination marker?\nA. It must be rescored as wakefulness.\nB. It must be rescored as N1.\nC. It must be rescored as N2.\nD. It may continue to be scored as stage R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_10.json b/sleep_assessment/cases/case43/case43_10.json deleted file mode 100644 index e0dcc68b2c7cc28d0df8825d062813dceb8e242a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_10.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q10", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about sawtooth waves is correct?\nA. They are typically 11-16 Hz and are required for N2.\nB. They are typically 2-6 Hz, are most prominent centrally, and support REM scoring but are not required for REM.\nC. Their presence is sufficient to diagnose REM sleep behavior disorder.\nD. They occur only in N3 and are usually accompanied by high-amplitude slow waves." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_11.json b/sleep_assessment/cases/case43/case43_11.json deleted file mode 100644 index 933b106e9b796fc98f1b6d742868a834d27e3231..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_11.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q11", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which option best matches the AASM arousal scoring rule?\nA. An increase in heart rate alone is sufficient to score an arousal.\nB. Any EEG frequency shift lasting at least 1 second is sufficient.\nC. An abrupt shift to alpha, theta, or activity above 16 Hz other than spindles lasts at least 3 seconds after at least 10 seconds of stable sleep; in stage R, at least 1 second of increased chin EMG is also required.\nD. Any sleep spindle is followed by an arousal." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_12.json b/sleep_assessment/cases/case43/case43_12.json deleted file mode 100644 index e32aec6ea7b520801c8ada43f2cc5a4542d8d905..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_12.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q12", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: What is the core adult PSG scoring criterion for apnea?\nA. Airflow decreases by at least 50% for at least 5 seconds with desaturation.\nB. Airflow decreases by at least 30% for at least 10 seconds with an arousal.\nC. The respiratory sensor signal decreases by at least 90% from baseline for at least 90% of the event duration, and the event lasts at least 10 seconds.\nD. Any oxygen desaturation of at least 3% defines an apnea." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_13.json b/sleep_assessment/cases/case43/case43_13.json deleted file mode 100644 index 0477ea2c9e13dc617f9ea13b61f5310354058158..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_13.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q13", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement correctly distinguishes obstructive, central, and mixed apnea?\nA. Central apnea has persistent or increasing respiratory effort throughout the event.\nB. The three types are distinguished only by the magnitude of SpO2 reduction.\nC. Obstructive apnea has no respiratory effort throughout the event.\nD. Mixed apnea begins without respiratory effort and is followed by resumption of respiratory effort." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_14.json b/sleep_assessment/cases/case43/case43_14.json deleted file mode 100644 index 6348b1c54b5691a2735026b2ad29fe7db5cd828a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_14.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q14", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which option gives the recommended adult hypopnea scoring rule?\nA. Airflow decreases by at least 30% for at least 10 seconds and is accompanied by at least 3% desaturation or an arousal.\nB. Thoracoabdominal paradox and snoring must both be present.\nC. Hypopnea can be scored only when SpO2 decreases by at least 4%; an arousal does not qualify.\nD. Airflow decreases by at least 90% for at least 10 seconds." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_15.json b/sleep_assessment/cases/case43/case43_15.json deleted file mode 100644 index 82c68cf04c99a09252547eb7b1bd615f358787d8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_15.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q15", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which option best defines a respiratory effort-related arousal (RERA)?\nA. Every snoring event is a RERA.\nB. Every central apnea is a RERA.\nC. Every respiratory event with at least 3% SpO2 reduction is a RERA.\nD. A sequence lasting at least 10 seconds with increasing respiratory effort or flattening of the inspiratory nasal-pressure waveform that leads to an arousal but does not meet apnea or hypopnea criteria." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_16.json b/sleep_assessment/cases/case43/case43_16.json deleted file mode 100644 index 5b9453c21b1d436a27a9f660f0bfa740b7f86096..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_16.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q16", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about scoring sleep respiratory events during supplemental oxygen is correct?\nA. Supplemental oxygen may blunt desaturation; if a diagnostic night is performed with oxygen, this should be noted in the report summary.\nB. All hypopneas should be rescored as apneas whenever oxygen is used.\nC. Supplemental oxygen makes desaturation more pronounced and therefore makes hypopnea easier to score.\nD. The AASM requires subtracting 3% from every SpO2 value before scoring patients receiving oxygen." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_17.json b/sleep_assessment/cases/case43/case43_17.json deleted file mode 100644 index e7daedc418d422d7d207a4304fc39205ba398c15..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_17.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q17", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which adult OSA severity classification by AHI is correct?\nA. Mild: 5-14.9/h; moderate: 15-29.9/h; severe: at least 30/h.\nB. Mild: 0-5/h; moderate: 6-20/h; severe: greater than 20/h.\nC. Mild: 5-9.9/h; moderate: 10-19.9/h; severe: at least 20/h.\nD. Mild: 1-4.9/h; moderate: 5-14.9/h; severe: at least 15/h." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_18.json b/sleep_assessment/cases/case43/case43_18.json deleted file mode 100644 index e78d4fa2d7d02b634c2ca31e57774da33b41f4ae..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_18.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q18", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about body position and OSA is best supported by the literature?\nA. Adult OSA is usually worse in the lateral position and milder when supine.\nB. Position affects OSA only in children, not in adults.\nC. Adult OSA is often worse when supine and milder in the lateral position.\nD. Position affects snoring but not OSA severity." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_19.json b/sleep_assessment/cases/case43/case43_19.json deleted file mode 100644 index 5f62091e427fa079559a27d1a22c388e58dd3523..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_19.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q19", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: According to the 2024 Australasian Sleep Association guideline, which set best represents the minimum channels for an adult Type 1 laboratory diagnostic PSG?\nA. Video alone is sufficient, so EEG, EOG, and EMG are unnecessary.\nB. Nasal airflow, oximetry, and a thoracic belt alone are equivalent to PSG.\nC. At least 3 EEG channels, 2 EOG channels, 1 chin EMG channel, 2 leg EMG channels, ECG, oximetry, airflow, thoracic and abdominal movement, body position, and snoring.\nD. One EEG channel, one EOG channel, no leg EMG, and no ECG." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_20.json b/sleep_assessment/cases/case43/case43_20.json deleted file mode 100644 index 53258416ef5be1a181d21a42f9092db3c036f8e6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_20.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q20", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about digital sampling of sleep signals is consistent with the Nyquist theorem?\nA. The sampling rate only needs to equal the highest frequency of interest.\nB. The sampling rate is determined mainly by screen resolution and is unrelated to signal frequency.\nC. A lower sampling rate always reduces distortion.\nD. The sampling rate should be greater than twice the highest frequency of interest." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_21.json b/sleep_assessment/cases/case43/case43_21.json deleted file mode 100644 index 156df2baf962207f070ec5f183792e49aedc61f6..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_21.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q21", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: According to the AAST PSG technical guideline, which sampling-rate setting is most appropriate for EEG, EOG, EMG, ECG, and snoring channels?\nA. Minimum 200 Hz; ideal or recommended 500 Hz.\nB. Exactly 1000 Hz is mandatory.\nC. Minimum 25 Hz; recommended 50 Hz.\nD. Minimum 100 Hz; recommended 128 Hz." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_22.json b/sleep_assessment/cases/case43/case43_22.json deleted file mode 100644 index d32b91e7e777a1db00113f3eb3678680f42de896..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_22.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q22", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: What is the most likely consequence of incorrectly lowering the EEG high-frequency filter cutoff from 35 Hz to 15 Hz?\nA. Sleep-spindle detail is preserved more effectively.\nB. EEG components near 15 Hz, including sleep spindles, are markedly attenuated and distorted.\nC. Only SpO2 is affected; EEG is unchanged.\nD. Electrode impedance improves without any spectral effect." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_23.json b/sleep_assessment/cases/case43/case43_23.json deleted file mode 100644 index 1a3353acf81268a4fe9874d5d4f4f5df32f22ac1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_23.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q23", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement best matches technical guidance for electrode impedance in adult PSG?\nA. The upper limit is 5 kOhm for EEG/EOG and 10 kOhm for EMG, and paired electrode impedances should be matched as closely as possible.\nB. EEG may be as high as 50 kOhm, while EMG must be below 1 kOhm.\nC. All channels must have impedances of at least 20 kOhm to remain stable.\nD. Impedance affects only ECG and does not affect EEG, EOG, or EMG." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_24.json b/sleep_assessment/cases/case43/case43_24.json deleted file mode 100644 index 7f47dbcb5cb4a9c3aebd7557bb96f2daa3931bad..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_24.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q24", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: If a PSG or sleep-EEG channel suddenly shows a nonphysiologic step-like deflection and a loose electrode is suspected, what should be considered first?\nA. A typical K-complex.\nB. A lead-pop or loose-electrode artifact.\nC. A sawtooth wave.\nD. New cortical slow-wave activity." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_25.json b/sleep_assessment/cases/case43/case43_25.json deleted file mode 100644 index d7ef52dee4804401383e0a8c32bc66e0f2a043f5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_25.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q25", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which option correctly defines a significant leg movement (LM) event according to the AASM?\nA. It lasts 0.5-10 seconds and the EMG amplitude rises at least 8 microvolts above resting baseline.\nB. Any movement associated with an arousal is an LM regardless of duration.\nC. An amplitude increase greater than 2 microvolts is sufficient.\nD. It must last longer than 30 seconds." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_26.json b/sleep_assessment/cases/case43/case43_26.json deleted file mode 100644 index 6b2890c0cd4c864023fb976cb152da7352112bc1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_26.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q26", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which criterion defines a periodic leg movement series?\nA. More than 10 leg movements over the entire night are sufficient.\nB. At least 4 consecutive LMs with onset intervals of 5-90 seconds; movements in the two legs beginning less than 5 seconds apart count as one movement.\nC. The interval must be fixed at exactly 20 seconds.\nD. At least 2 leg movements are sufficient, with no restriction on interval." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_27.json b/sleep_assessment/cases/case43/case43_27.json deleted file mode 100644 index f52ca3a18127879edbf3c63dde8f8d6494033ff0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_27.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q27", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which AASM rule applies to scoring leg movements adjacent to respiratory events?\nA. Exclusion is needed only during REM sleep.\nB. Any movement accompanied by desaturation must be included as a PLM.\nC. Every movement occurring during sleep counts as a PLM.\nD. A leg movement occurring from 0.5 seconds before respiratory-event onset through 0.5 seconds after event end should not be scored as an LM." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_28.json b/sleep_assessment/cases/case43/case43_28.json deleted file mode 100644 index 2af9194518dfec5caa70c027d3bb090e97db6aac..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_28.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q28", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement correctly distinguishes restless legs syndrome (RLS) from periodic limb movement disorder (PLMD)?\nA. An elevated PLMI always proves RLS.\nB. RLS requires PSG, whereas PLMD does not.\nC. RLS and PLMD are synonymous.\nD. RLS is a clinical diagnosis, whereas PLMD diagnosis requires PSG evidence together with relevant sleep disturbance or daytime impairment." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_29.json b/sleep_assessment/cases/case43/case43_29.json deleted file mode 100644 index 74c842431062c503590c54758e2fa8d53acd600c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_29.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q29", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement most accurately describes the diagnosis of REM sleep behavior disorder (RBD)?\nA. A transient burst of chin EMG activity alone confirms RBD.\nB. Frequent dreaming and a high REM percentage confirm RBD.\nC. There must be recurrent sleep-related vocalization or complex motor behavior occurring or presumed to occur during REM, PSG-confirmed REM sleep without atonia, and no better explanation by another sleep disorder, mental disorder, medication, or substance.\nD. A patient's report of frequent dream-related movement alone confirms RBD." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_30.json b/sleep_assessment/cases/case43/case43_30.json deleted file mode 100644 index 0e9a14f7830d4a890c9a4e9e0c47055cf6eb0775..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_30.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q30", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which approach can improve diagnostic yield during PSG for suspected RBD?\nA. Retain only a single chin EMG channel and remove limb EMG channels.\nB. Use an expanded SINBAR EMG montage, such as simultaneous mentalis, flexor digitorum superficialis, and extensor digitorum brevis recordings.\nC. Remove video monitoring to reduce interference.\nD. Assess only changes in SpO2." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_31.json b/sleep_assessment/cases/case43/case43_31.json deleted file mode 100644 index 6150b198b514a2edf9d5d29145d1dea5f5eff348..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_31.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q31", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about REM muscle activity and RBD is correct?\nA. Brief, small muscle bursts may occur in normal REM; excessive tonic or phasic EMG activity during REM constitutes RWA/RSWA and must be interpreted with the clinical presentation when assessing RBD.\nB. Low chin muscle tone is absolutely incompatible with RBD.\nC. Normal REM never contains any brief muscle burst.\nD. Any anterior tibialis EMG activity proves RBD." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_32.json b/sleep_assessment/cases/case43/case43_32.json deleted file mode 100644 index 6f5d88cb0a9ca9e33823eececefabc618a5b8f28..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_32.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q32", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement best matches current commonly used interpretation of the MSLT for narcolepsy?\nA. A mean sleep latency of 8 minutes or less alone diagnoses narcolepsy.\nB. A mean sleep latency of 8 minutes or less plus at least 2 SOREMPs supports narcolepsy; these may be 2 on the MSLT, or 1 on the MSLT plus 1 on the preceding-night PSG.\nC. A mean latency greater than 8 minutes plus 1 SOREMP diagnoses narcolepsy.\nD. The MSLT requires only one nap trial." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_33.json b/sleep_assessment/cases/case43/case43_33.json deleted file mode 100644 index 30a8581cf41f897facc84e8c71be9f368c7bc13a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_33.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q33", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement correctly describes the two-process model of sleep regulation?\nA. Both processes describe only REM regulation.\nB. Process S is circadian rhythm and Process C is sleep debt.\nC. The model assumes that sleep is completely independent of the biological clock.\nD. Process S is homeostatic sleep pressure that accumulates during wakefulness, while Process C is controlled by the circadian pacemaker." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_34.json b/sleep_assessment/cases/case43/case43_34.json deleted file mode 100644 index 1dbb60f6b5c117a700b79a02b30098b05ee4c234..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_34.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q34", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which measure is widely regarded as the gold standard for assessing human circadian phase?\nA. Dim light melatonin onset (DLMO).\nB. Total PSQI score.\nC. Nightly REM percentage.\nD. Sleep-onset latency." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "A" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_35.json b/sleep_assessment/cases/case43/case43_35.json deleted file mode 100644 index 784d347d8062d9632585bfd646a61d586fd882b2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_35.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q35", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about adenosine and sleep homeostasis is correct?\nA. Adenosine decreases during prolonged wakefulness and rises during sleep.\nB. Adenosine is unrelated to sleep, and caffeine acts mainly by increasing melatonin.\nC. Adenosine accumulates during wakefulness and promotes sleep pressure, while caffeine alters sleep-wake regulation by antagonizing adenosine signaling.\nD. Adenosine regulates only REM and has no effect on NREM." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_36.json b/sleep_assessment/cases/case43/case43_36.json deleted file mode 100644 index c26dbbea6b792b26da2548da001d5c15691829f7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_36.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q36", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement most accurately describes the role of the VLPO in sleep-wake regulation?\nA. The VLPO is a wake-promoting nucleus that primarily releases dopamine.\nB. The VLPO controls only REM and has no role in NREM.\nC. The VLPO regulates respiration only, not sleep.\nD. Sleep-active VLPO neurons express GABA and galanin and primarily promote NREM sleep." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "D" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_37.json b/sleep_assessment/cases/case43/case43_37.json deleted file mode 100644 index 79a0415fa056a7c05bacec24896a1a444ee53a90..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_37.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q37", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about orexin (hypocretin) and narcolepsy is correct?\nA. Orexin affects appetite only and does not stabilize sleep-wake states.\nB. Narcolepsy type 1 is primarily associated with loss of hypothalamic orexin-producing neurons or orexin deficiency.\nC. Orexin deficiency causes REM sleep to disappear completely.\nD. Orexin deficiency usually causes insomnia rather than sleepiness." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_38.json b/sleep_assessment/cases/case43/case43_38.json deleted file mode 100644 index c9f69c094b281638066e6adca62c639c4ab5f783..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_38.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q38", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement best describes the overall autonomic pattern during normal sleep?\nA. HRV changes occur only during wakefulness; sleep stage has no effect on HRV.\nB. NREM, especially slow-wave sleep, is relatively parasympathetic-dominant, whereas REM shows greater sympathetic fluctuation and autonomic instability.\nC. NREM, especially slow-wave sleep, is sympathetic-dominant, whereas REM is parasympathetic-dominant.\nD. NREM and REM have identical autonomic patterns." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "B" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_39.json b/sleep_assessment/cases/case43/case43_39.json deleted file mode 100644 index 2b48083bd67c0cf36c4ff8964c5c0989c7e890f1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_39.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q39", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement about the function of sleep spindles is best supported by current evidence?\nA. Sleep spindles are used only to define N2 and have no relation to memory.\nB. Fewer sleep spindles necessarily improve memory.\nC. Sleep spindles are associated with brain plasticity and consolidation of declarative and procedural memory.\nD. Sleep spindles occur only in epilepsy." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case43/case43_40.json b/sleep_assessment/cases/case43/case43_40.json deleted file mode 100644 index bc7bfad359f19b5f65451e8df6de609326c242af..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case43/case43_40.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "meta_info": { - "case_id": "Q40", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Sleep_QA" - }, - "agent_input": { - "data_path": "", - "instruction": "This is a standalone sleep-medicine multiple-choice question. No sleep data file is required. Select the single correct answer from A, B, C, or D, and clearly state your final option.\n\nQuestion: Which statement best reflects current evidence on sleep, slow-wave activity, and brain CSF or glymphatic clearance?\nA. Glymphatic clearance is most active only during full wakefulness.\nB. The glymphatic system has no established association with sleep stage.\nC. Sleep and slow-wave activity are associated with CSF dynamics and the glymphatic hypothesis, but the direction and magnitude of clearance effects and their causal role in humans remain under investigation and debate.\nD. Only REM sleep is related to the glymphatic system." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep-medicine multiple-choice responses.\n\n### TASK\nExtract only the agent's final selected option.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown or explanatory text.\n3. The key must be exactly \"selected_option\".\n4. The value must be one of \"A\", \"B\", \"C\", \"D\", or null.\n5. If multiple options are mentioned, extract the explicitly identified final answer.\n6. If the final option is missing or ambiguous, return null.\n\n### OUTPUT TEMPLATE\n{\"selected_option\": }", - "metrics": [ - { - "metric_id": "sleep_qa_option_accuracy", - "type": "categorical_check", - "target_key": "selected_option", - "weight": 100, - "params": { - "gt_value": "C" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_01.json b/sleep_assessment/cases/case5/case5_01.json deleted file mode 100644 index a29814c7cabc476bdf5fa6e3b433f22ca85b63d2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_01.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 249.0, - "tolerance": 12.450000000000001 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 19.155844155844157, - "tolerance": 0.9577922077922079 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 165.5, - "tolerance": 8.275 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_02.json b/sleep_assessment/cases/case5/case5_02.json deleted file mode 100644 index 0392f3c3ccc0be1a424de09c498c9de82b5fb9b7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_02.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 308.5, - "tolerance": 15.425 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 14.186369958275382, - "tolerance": 0.7093184979137691 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 105.5, - "tolerance": 5.275 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_03.json b/sleep_assessment/cases/case5/case5_03.json deleted file mode 100644 index 5bdd267cf7bb2cdedb5292c31b4ae365da93b028..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_03.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 292.0, - "tolerance": 14.600000000000001 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 27.990135635018497, - "tolerance": 1.399506781750925 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 158.0, - "tolerance": 7.9 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_04.json b/sleep_assessment/cases/case5/case5_04.json deleted file mode 100644 index 875737dd2c8cad491054385ad84582ab7a84efcd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_04.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 352.5, - "tolerance": 17.625 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 24.59893048128342, - "tolerance": 1.2299465240641712 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 79.5, - "tolerance": 3.975 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_05.json b/sleep_assessment/cases/case5/case5_05.json deleted file mode 100644 index b4db5ed151d06b04de92419665aae0bb432c8815..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_05.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 268.5, - "tolerance": 13.425 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 7.253886010362694, - "tolerance": 0.3626943005181347 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 126.5, - "tolerance": 6.325 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_06.json b/sleep_assessment/cases/case5/case5_06.json deleted file mode 100644 index 03cc5c38e9a316590637b54047f27f1e10cf457b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_06.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 281.0, - "tolerance": 14.05 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 20.056899004267425, - "tolerance": 1.0028449502133714 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 73.5, - "tolerance": 3.6750000000000003 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_07.json b/sleep_assessment/cases/case5/case5_07.json deleted file mode 100644 index 6d2f09936dafd09c8a233c9490307708d7d20464..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_07.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 300.0, - "tolerance": 15.0 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 22.48062015503876, - "tolerance": 1.124031007751938 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 109.5, - "tolerance": 5.4750000000000005 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_08.json b/sleep_assessment/cases/case5/case5_08.json deleted file mode 100644 index 1a0a18a3369d9c6f23d333a61cf85b0c1178c842..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_08.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 199.5, - "tolerance": 9.975000000000001 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 27.586206896551722, - "tolerance": 1.3793103448275863 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 263.0, - "tolerance": 13.15 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_09.json b/sleep_assessment/cases/case5/case5_09.json deleted file mode 100644 index 7700045b33eb9ed105fbcd5ee0f0154d85ffee0b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_09.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 344.5, - "tolerance": 17.225 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 26.38888888888889, - "tolerance": 1.3194444444444446 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 46.0, - "tolerance": 2.3000000000000003 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_10.json b/sleep_assessment/cases/case5/case5_10.json deleted file mode 100644 index 4f63ce1edbe16cb81b0dec4eb6ffd5cbec8decfa..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_10.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 315.5, - "tolerance": 15.775 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 16.75461741424802, - "tolerance": 0.837730870712401 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 186.0, - "tolerance": 9.3 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_11.json b/sleep_assessment/cases/case5/case5_11.json deleted file mode 100644 index 9508480bc551c5f0f27f9b8599cf79893c91b03d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_11.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 324.5, - "tolerance": 16.225 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 13.581890812250332, - "tolerance": 0.6790945406125166 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 114.0, - "tolerance": 5.7 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_12.json b/sleep_assessment/cases/case5/case5_12.json deleted file mode 100644 index a5cba3e672226688fd202c041e56b415e61c6588..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_12.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 151.5, - "tolerance": 7.575 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 16.75824175824176, - "tolerance": 0.837912087912088 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 37.5, - "tolerance": 1.875 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_13.json b/sleep_assessment/cases/case5/case5_13.json deleted file mode 100644 index a543d4c898195cf47eeb849a5060dab681f52133..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_13.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 315.5, - "tolerance": 15.775 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 11.99442119944212, - "tolerance": 0.599721059972106 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 142.0, - "tolerance": 7.1000000000000005 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_14.json b/sleep_assessment/cases/case5/case5_14.json deleted file mode 100644 index 290b077e2c658a932d61825df7870536a3a3dea8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_14.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 255.0, - "tolerance": 12.75 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 15.282392026578073, - "tolerance": 0.7641196013289037 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 45.0, - "tolerance": 2.25 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_15.json b/sleep_assessment/cases/case5/case5_15.json deleted file mode 100644 index 8c6d1a50d5e074d180355abc81651b8ff4c1dd11..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_15.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 324.5, - "tolerance": 16.225 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 12.297297297297296, - "tolerance": 0.6148648648648649 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 126.5, - "tolerance": 6.325 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_16.json b/sleep_assessment/cases/case5/case5_16.json deleted file mode 100644 index 9ed3a9944509829edca1adfca480727aa15911ec..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_16.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 379.0, - "tolerance": 18.95 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 19.275825346112885, - "tolerance": 0.9637912673056444 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 53.5, - "tolerance": 2.6750000000000003 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_17.json b/sleep_assessment/cases/case5/case5_17.json deleted file mode 100644 index 77b59b092bd09672ab890c8f19b394f9883bee97..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_17.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 356.5, - "tolerance": 17.825 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 25.340314136125656, - "tolerance": 1.2670157068062828 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 42.0, - "tolerance": 2.1 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_18.json b/sleep_assessment/cases/case5/case5_18.json deleted file mode 100644 index 0e647f060b47557ac0981dc3500dc94f73871672..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_18.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 317.5, - "tolerance": 15.875 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 29.678848283499445, - "tolerance": 1.4839424141749724 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 81.5, - "tolerance": 4.075 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_19.json b/sleep_assessment/cases/case5/case5_19.json deleted file mode 100644 index 1e02702db3dc9174b67fe4ba954d4e10d3c56d1b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_19.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 328.0, - "tolerance": 16.400000000000002 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 27.19200887902331, - "tolerance": 1.3596004439511655 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 71.5, - "tolerance": 3.575 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_20.json b/sleep_assessment/cases/case5/case5_20.json deleted file mode 100644 index 862d280070a4bc88b12bf62408cf291ef076e1e2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_20.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 369.5, - "tolerance": 18.475 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 27.263779527559056, - "tolerance": 1.3631889763779528 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 85.0, - "tolerance": 4.25 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_21.json b/sleep_assessment/cases/case5/case5_21.json deleted file mode 100644 index 9d6a8940ace2dbe0cf9ce19f2b0f7b4843d0bc7f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_21.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 198.5, - "tolerance": 9.925 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 16.94560669456067, - "tolerance": 0.8472803347280335 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 82.0, - "tolerance": 4.1000000000000005 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_22.json b/sleep_assessment/cases/case5/case5_22.json deleted file mode 100644 index 60ec1bb068247eebdf742b25411f89e9f86a3207..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_22.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 229.5, - "tolerance": 11.475000000000001 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 0.21739130434782608, - "tolerance": 0.010869565217391304 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 352.0, - "tolerance": 17.6 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_23.json b/sleep_assessment/cases/case5/case5_23.json deleted file mode 100644 index 11de5ed67be603fd0b49fb4f1e7d0b29b71d5d76..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_23.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 225.5, - "tolerance": 11.275 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 0.22123893805309736, - "tolerance": 0.011061946902654869 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 101.0, - "tolerance": 5.050000000000001 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_24.json b/sleep_assessment/cases/case5/case5_24.json deleted file mode 100644 index 75f1a6e599153b3430891aa0d9c7d08aff462e10..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_24.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 336.5, - "tolerance": 16.825 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 11.795543905635649, - "tolerance": 0.5897771952817824 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 55.0, - "tolerance": 2.75 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case5/case5_25.json b/sleep_assessment/cases/case5/case5_25.json deleted file mode 100644 index d28faeb56b404fac441d82656072af03aa64186d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case5/case5_25.json +++ /dev/null @@ -1,47 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate three sleep-stage metrics for the recording: NREM duration in minutes, REM sleep ratio as a percentage of total sleep time, and REM latency in minutes. Define REM latency as the time from the first sleep epoch to the first REM epoch. Please clearly report all three final values." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract three values from the agent's report:\n1) NREM duration in minutes\n2) REM sleep ratio as a percentage of total sleep time\n3) REM latency in minutes\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"nrem_duration_min\", \"rem_sleep_ratio_percent\", and \"rem_latency_min\".\n5. All values must be FLOATS or null.\n6. \"nrem_duration_min\" and \"rem_latency_min\" are measured in minutes.\n7. \"rem_sleep_ratio_percent\" is a percentage value; if the report says 18.5%, output 18.5.\n8. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"nrem_duration_min\": , \"rem_sleep_ratio_percent\": , \"rem_latency_min\": }", - "metrics": [ - { - "metric_id": "nrem_duration_accuracy", - "type": "numeric_check", - "target_key": "nrem_duration_min", - "weight": 34, - "params": { - "gt_value": 399.0, - "tolerance": 19.950000000000003 - } - }, - { - "metric_id": "rem_sleep_ratio_accuracy", - "type": "numeric_check", - "target_key": "rem_sleep_ratio_percent", - "weight": 33, - "params": { - "gt_value": 11.333333333333334, - "tolerance": 0.5666666666666668 - } - }, - { - "metric_id": "rem_latency_accuracy", - "type": "numeric_check", - "target_key": "rem_latency_min", - "weight": 33, - "params": { - "gt_value": 105.0, - "tolerance": 5.25 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_01.json b/sleep_assessment/cases/case6/case6_01.json deleted file mode 100644 index 3ea82f1f81b7895d3f11dd7ab2212ac171b5d56f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_01.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "W" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_02.json b/sleep_assessment/cases/case6/case6_02.json deleted file mode 100644 index 42fa7318ce63a9673887f1a6400b35aa92c56596..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_02.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_03.json b/sleep_assessment/cases/case6/case6_03.json deleted file mode 100644 index 45987926f2af7a397518599dfb41a5124aea4b67..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_03.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_04.json b/sleep_assessment/cases/case6/case6_04.json deleted file mode 100644 index 56759bcbf357f1511c333dff6e42d92b8d126a84..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_04.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_05.json b/sleep_assessment/cases/case6/case6_05.json deleted file mode 100644 index 44be1a3c6acf83af094de64869299dfb81d6e62b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_05.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "W" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_06.json b/sleep_assessment/cases/case6/case6_06.json deleted file mode 100644 index 789596439599386f280a4502fb86d3ec4ab57d53..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_06.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_07.json b/sleep_assessment/cases/case6/case6_07.json deleted file mode 100644 index 6a250e832b8f0233e8867a912546cd2d0a70bce5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_07.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_08.json b/sleep_assessment/cases/case6/case6_08.json deleted file mode 100644 index da92c9aebec9c6b829a89229cc8eb5fb653c0705..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_08.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "W" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_09.json b/sleep_assessment/cases/case6/case6_09.json deleted file mode 100644 index 0640e0b4de483557d1b6a8601b16013fc0ec63ff..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_09.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_10.json b/sleep_assessment/cases/case6/case6_10.json deleted file mode 100644 index 8d86b7860c3693364a3665e5fd58b549bc56787f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_10.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_11.json b/sleep_assessment/cases/case6/case6_11.json deleted file mode 100644 index 8e256cf760bcb89f7d460e7a957208606c39136f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_11.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_12.json b/sleep_assessment/cases/case6/case6_12.json deleted file mode 100644 index a019ae0fb77e26801003e292d85309cc7a97727c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_12.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "W" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_13.json b/sleep_assessment/cases/case6/case6_13.json deleted file mode 100644 index 118bee7ced9dae362d5cffeb4bad6c69e143cda1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_13.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "W" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_14.json b/sleep_assessment/cases/case6/case6_14.json deleted file mode 100644 index f3a5d246372d62817fedee1d2216a643fe17ac87..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_14.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N3" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_15.json b/sleep_assessment/cases/case6/case6_15.json deleted file mode 100644 index 01f4d90fa64a8bfaabaa39ba57ffc9431f36c3c1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_15.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "W" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_16.json b/sleep_assessment/cases/case6/case6_16.json deleted file mode 100644 index 4db2b5679570f32a453badc41f1de3bd8cc5fd17..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_16.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_17.json b/sleep_assessment/cases/case6/case6_17.json deleted file mode 100644 index 895fabfbd5b96099e372e1a8f57e33142cd95381..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_17.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_18.json b/sleep_assessment/cases/case6/case6_18.json deleted file mode 100644 index 9073932f1832c386bbc6aa5e9e4879a800bd5749..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_18.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_19.json b/sleep_assessment/cases/case6/case6_19.json deleted file mode 100644 index 630b74657b22b56f9512759e8b4fc8a202b2baf0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_19.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_20.json b/sleep_assessment/cases/case6/case6_20.json deleted file mode 100644 index 6232d3c856888e4b17decaf9f3f8bfa14c096d14..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_20.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "R" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_21.json b/sleep_assessment/cases/case6/case6_21.json deleted file mode 100644 index 21c9f9a7ba3baf8a30828e2c7dc3e33c7b284def..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_21.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "W" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_22.json b/sleep_assessment/cases/case6/case6_22.json deleted file mode 100644 index 94ed4993e182e149d7482094d7e52b2b3b3b256d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_22.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "W" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_23.json b/sleep_assessment/cases/case6/case6_23.json deleted file mode 100644 index 6c4ccce752488619939a22402d3f1a586248114b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_23.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "W" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N2" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_24.json b/sleep_assessment/cases/case6/case6_24.json deleted file mode 100644 index 3b07f3c7d25c47a1bb89a710b450fe8de6d40c6a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_24.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N3" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case6/case6_25.json b/sleep_assessment/cases/case6/case6_25.json deleted file mode 100644 index 8b3cee71f5bc45d873703fe6a7b455cd95b64348..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case6/case6_25.json +++ /dev/null @@ -1,35 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to analyze the whole-night stage distribution. Determine the dominant sleep stage and the secondary sleep stage across the entire recording, and report both using the fixed stage labels W, N1, N2, N3, and R." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two stage labels from the agent's report:\n1) the dominant sleep stage across the whole night\n2) the secondary sleep stage across the whole night\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"dominant_sleep_stage\" and \"secondary_sleep_stage\".\n5. Each value must be exactly one of: \"W\", \"N1\", \"N2\", \"N3\", \"R\", or null.\n6. Normalize any synonyms to the fixed field set above. For example: Wake -> W, REM -> R.\n7. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"dominant_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>, \"secondary_sleep_stage\": <\"W\"|\"N1\"|\"N2\"|\"N3\"|\"R\"|null>}", - "metrics": [ - { - "metric_id": "dominant_stage_accuracy", - "type": "categorical_check", - "target_key": "dominant_sleep_stage", - "weight": 60, - "params": { - "gt_value": "N2" - } - }, - { - "metric_id": "secondary_stage_accuracy", - "type": "categorical_check", - "target_key": "secondary_sleep_stage", - "weight": 40, - "params": { - "gt_value": "N1" - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_01.json b/sleep_assessment/cases/case7/case7_01.json deleted file mode 100644 index c0a86fc5a55e3a45efbbdc6a06fad2d923227d4c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_01.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 1.155844155844156, - "tolerance": 0.0577922077922078 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The ground-truth light/deep sleep ratio is 1.155844, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is relatively high and that the sleep shows an enhanced restorative tendency. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_02.json b/sleep_assessment/cases/case7/case7_02.json deleted file mode 100644 index c329f490bf453107c17ad82e24925bc1eee5d441..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_02.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 2.929936305732484, - "tolerance": 0.1464968152866242 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The ground-truth light/deep sleep ratio is 2.929936, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that the sleep structure is balanced or normal. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_03.json b/sleep_assessment/cases/case7/case7_03.json deleted file mode 100644 index 52dbee1ddfad5c4fc65b33a9a6e11f6e479f9e03..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_03.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 2.3757225433526012, - "tolerance": 0.11878612716763007 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The ground-truth light/deep sleep ratio is 2.375723, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that the sleep structure is balanced or normal. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_04.json b/sleep_assessment/cases/case7/case7_04.json deleted file mode 100644 index 70e43873234b67d807cf342e5c541a1f9de12cb7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_04.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 2.294392523364486, - "tolerance": 0.11471962616822431 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. The ground-truth light/deep sleep ratio is 2.294393, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that the sleep structure is balanced or normal. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_05.json b/sleep_assessment/cases/case7/case7_05.json deleted file mode 100644 index 105b97c3688427fbcd9f4285d4d97dfebb683e67..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_05.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 2.274390243902439, - "tolerance": 0.11371951219512196 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The ground-truth light/deep sleep ratio is 2.274390, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that the sleep structure is balanced or normal. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_06.json b/sleep_assessment/cases/case7/case7_06.json deleted file mode 100644 index e7da1e15e1be5a669b457d7f908bac1072b50fba..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_06.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 23.434782608695652, - "tolerance": 1.1717391304347826 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_01.edf. The ground-truth light/deep sleep ratio is 23.434783, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_07.json b/sleep_assessment/cases/case7/case7_07.json deleted file mode 100644 index fdf26ba0070543e522c44e2367c49b333862807c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_07.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 1.6785714285714286, - "tolerance": 0.08392857142857144 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_02.edf. The ground-truth light/deep sleep ratio is 1.678571, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that the sleep structure is balanced or normal. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_08.json b/sleep_assessment/cases/case7/case7_08.json deleted file mode 100644 index e5d2862b8badb270258d738df90cad1346a1e749..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_08.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 1.66, - "tolerance": 0.083 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_03.edf. The ground-truth light/deep sleep ratio is 1.660000, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that the sleep structure is balanced or normal. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_09.json b/sleep_assessment/cases/case7/case7_09.json deleted file mode 100644 index 21fd035d5fa167fb6695da71126c4705af617570..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_09.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 9.765625, - "tolerance": 0.48828125 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_04.edf. The ground-truth light/deep sleep ratio is 9.765625, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_10.json b/sleep_assessment/cases/case7/case7_10.json deleted file mode 100644 index 3c7831286d33bca9d41113ee9771c45cd27fa3a3..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_10.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 3.2348993288590604, - "tolerance": 0.16174496644295303 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_05.edf. The ground-truth light/deep sleep ratio is 3.234899, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that sleep is somewhat shallow and that deep sleep is slightly insufficient. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_11.json b/sleep_assessment/cases/case7/case7_11.json deleted file mode 100644 index c4ebf19131c8fce7fbca5ba97107a60faf39a0d9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_11.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 3.475862068965517, - "tolerance": 0.17379310344827587 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The ground-truth light/deep sleep ratio is 3.475862, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that sleep is somewhat shallow and that deep sleep is slightly insufficient. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_12.json b/sleep_assessment/cases/case7/case7_12.json deleted file mode 100644 index afc5864385ae7be9a99220ba6de2a3f46a3f1e5d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_12.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 3.9672131147540983, - "tolerance": 0.19836065573770492 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The ground-truth light/deep sleep ratio is 3.967213, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that sleep is somewhat shallow and that deep sleep is slightly insufficient. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_13.json b/sleep_assessment/cases/case7/case7_13.json deleted file mode 100644 index c9ee4fcaaac2c525fb1a628823a3556938be3e94..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_13.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 1.0553745928338762, - "tolerance": 0.052768729641693816 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The ground-truth light/deep sleep ratio is 1.055375, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is relatively high and that the sleep shows an enhanced restorative tendency. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_14.json b/sleep_assessment/cases/case7/case7_14.json deleted file mode 100644 index 787d42e891f44e740412ad712c750706cad3eb0c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_14.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 0.6190476190476191, - "tolerance": 0.05 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The ground-truth light/deep sleep ratio is 0.619048, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is unusually high and that the overall sleep structure is atypical. It must not describe the structure as balanced, normal, or shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_15.json b/sleep_assessment/cases/case7/case7_15.json deleted file mode 100644 index c49bc0010f9d8cc3cf1364383cdbddbc2360658e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_15.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 5.555555555555555, - "tolerance": 0.2777777777777778 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The ground-truth light/deep sleep ratio is 5.555556, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_16.json b/sleep_assessment/cases/case7/case7_16.json deleted file mode 100644 index cc32e3c15f935d6413ea066fa836348e13a62d92..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_16.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 4.191780821917808, - "tolerance": 0.2095890410958904 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_01.edf. The ground-truth light/deep sleep ratio is 4.191781, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_17.json b/sleep_assessment/cases/case7/case7_17.json deleted file mode 100644 index 220bf60f49758a0932b9f1a69c7e0e7d89365948..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_17.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 3.0977011494252875, - "tolerance": 0.15488505747126438 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_02.edf. The ground-truth light/deep sleep ratio is 3.097701, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that sleep is somewhat shallow and that deep sleep is slightly insufficient. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_18.json b/sleep_assessment/cases/case7/case7_18.json deleted file mode 100644 index 4af68e9f37437174ebe096064023e82748a55c82..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_18.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 4.120967741935484, - "tolerance": 0.2060483870967742 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_03.edf. The ground-truth light/deep sleep ratio is 4.120968, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_19.json b/sleep_assessment/cases/case7/case7_19.json deleted file mode 100644 index 68279e8d65a1209af7665753d90eb164d5de3219..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_19.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 9.25, - "tolerance": 0.4625 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_04.edf. The ground-truth light/deep sleep ratio is 9.250000, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_20.json b/sleep_assessment/cases/case7/case7_20.json deleted file mode 100644 index 0c111fc37179638b5bd6f2b709869556e3090674..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_20.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 2.7897435897435896, - "tolerance": 0.13948717948717948 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_05.edf. The ground-truth light/deep sleep ratio is 2.789744, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that the sleep structure is balanced or normal. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_21.json b/sleep_assessment/cases/case7/case7_21.json deleted file mode 100644 index ecfac0ae2fd375ff046c71b38f1c0664de5d7724..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_21.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 4.513888888888889, - "tolerance": 0.22569444444444448 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_01.edf. The ground-truth light/deep sleep ratio is 4.513889, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_22.json b/sleep_assessment/cases/case7/case7_22.json deleted file mode 100644 index 1a11bffa7f832fd1c531ad40eb437d5107e71293..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_22.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 20.857142857142858, - "tolerance": 1.042857142857143 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_02.edf. The ground-truth light/deep sleep ratio is 20.857143, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_23.json b/sleep_assessment/cases/case7/case7_23.json deleted file mode 100644 index 09a6353805a46f794e85d33e5309e53d45613c04..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_23.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 6.274193548387097, - "tolerance": 0.31370967741935485 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_03.edf. The ground-truth light/deep sleep ratio is 6.274194, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_24.json b/sleep_assessment/cases/case7/case7_24.json deleted file mode 100644 index 4c4fb2ffae62baf33d07b7a59f32d33a47ebbe3b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_24.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 2.9127906976744184, - "tolerance": 0.14563953488372092 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_04.edf. The ground-truth light/deep sleep ratio is 2.912791, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that the sleep structure is balanced or normal. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case7/case7_25.json b/sleep_assessment/cases/case7/case7_25.json deleted file mode 100644 index 8b33e9d7c914c63cf6817c0aae70573e99857206..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case7/case7_25.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate the light-sleep to deep-sleep ratio for the whole night. Then interpret what this ratio suggests about the sleep architecture, including whether sleep appears balanced, relatively restorative, too shallow, or atypical. Please report the final ratio and give a brief explanation of the sleep quality implication." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the light-sleep to deep-sleep ratio from the agent's report.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The key must be EXACTLY \"light_deep_sleep_ratio\".\n5. The value must be a FLOAT or null.\n6. If the value is missing or cannot be found, return {\"light_deep_sleep_ratio\": null}.\n\n### OUTPUT TEMPLATE\n{\"light_deep_sleep_ratio\": }", - "metrics": [ - { - "metric_id": "light_deep_ratio_accuracy", - "type": "numeric_check", - "target_key": "light_deep_sleep_ratio", - "weight": 40, - "params": { - "gt_value": 7.0606060606060606, - "tolerance": 0.353030303030303 - } - }, - { - "metric_id": "sleep_quality_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 60, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_05.edf. The ground-truth light/deep sleep ratio is 7.060606, where the ratio is defined as (N1+N2)/N3. The correct response must explicitly conclude that deep sleep is markedly insufficient and that the sleep structure is abnormally shallow. Mark the response as correct only if its final conclusion matches that rule. If the response gives the opposite conclusion or contradicts that rule, mark it as incorrect. Output only a valid JSON object with exactly one key named status whose value is either True or False. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_01.json b/sleep_assessment/cases/case8/case8_01.json deleted file mode 100644 index cf5a8f0cfa0afef94d809979229ba83fb7e6ee9f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_01.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 101.5, - "tolerance": 5.075 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 43.5, - "tolerance": 2.1750000000000003 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 43.0, - "tolerance": 2.15 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_01.edf. The three longest uninterrupted sleep episodes are 101.500000, 43.500000, and 43.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_02.json b/sleep_assessment/cases/case8/case8_02.json deleted file mode 100644 index 762b7581b46729d5954114b16b566ab3b5facedd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_02.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 60.5, - "tolerance": 3.0250000000000004 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 37.0, - "tolerance": 1.85 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 34.5, - "tolerance": 1.725 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_02.edf. The three longest uninterrupted sleep episodes are 60.500000, 37.000000, and 34.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_03.json b/sleep_assessment/cases/case8/case8_03.json deleted file mode 100644 index a2a55236d60d4f2f9ae2b86ffc13a5d4260edd91..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_03.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 67.5, - "tolerance": 3.375 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 45.0, - "tolerance": 2.25 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 42.0, - "tolerance": 2.1 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_03.edf. The three longest uninterrupted sleep episodes are 67.500000, 45.000000, and 42.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_04.json b/sleep_assessment/cases/case8/case8_04.json deleted file mode 100644 index 12ab01b16fcb2cb608f20b765c1494e9f02d3df2..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_04.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 97.5, - "tolerance": 4.875 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 87.0, - "tolerance": 4.3500000000000005 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 74.0, - "tolerance": 3.7 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_04.edf. The three longest uninterrupted sleep episodes are 97.500000, 87.000000, and 74.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_05.json b/sleep_assessment/cases/case8/case8_05.json deleted file mode 100644 index 20910355b112a0028fe6dee99d96c66434b1921a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_05.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 68.0, - "tolerance": 3.4000000000000004 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 63.5, - "tolerance": 3.1750000000000003 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 57.5, - "tolerance": 2.875 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is ISRUC_05.edf. The three longest uninterrupted sleep episodes are 68.000000, 63.500000, and 57.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_06.json b/sleep_assessment/cases/case8/case8_06.json deleted file mode 100644 index 37ef8caef35c73af5c518ac17a51fee01d1c2fea..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_06.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 84.5, - "tolerance": 4.2250000000000005 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 70.5, - "tolerance": 3.5250000000000004 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 50.0, - "tolerance": 2.5 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_01.edf. The three longest uninterrupted sleep episodes are 84.500000, 70.500000, and 50.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_07.json b/sleep_assessment/cases/case8/case8_07.json deleted file mode 100644 index 99ed3261f3a207a5aeaffa3ad3cba1a59b12492f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_07.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 90.0, - "tolerance": 4.5 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 66.0, - "tolerance": 3.3000000000000003 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 64.0, - "tolerance": 3.2 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_02.edf. The three longest uninterrupted sleep episodes are 90.000000, 66.000000, and 64.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_08.json b/sleep_assessment/cases/case8/case8_08.json deleted file mode 100644 index bbfde4872db368f8dc6dc8f17b404eac4ac4d963..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_08.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 116.5, - "tolerance": 5.825 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 48.0, - "tolerance": 2.4000000000000004 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 33.5, - "tolerance": 1.675 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_03.edf. The three longest uninterrupted sleep episodes are 116.500000, 48.000000, and 33.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_09.json b/sleep_assessment/cases/case8/case8_09.json deleted file mode 100644 index d7df0c8c96202870c646abfce11f7a1cc5286e26..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_09.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 85.0, - "tolerance": 4.25 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 65.5, - "tolerance": 3.2750000000000004 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 53.5, - "tolerance": 2.6750000000000003 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_04.edf. The three longest uninterrupted sleep episodes are 85.000000, 65.500000, and 53.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_10.json b/sleep_assessment/cases/case8/case8_10.json deleted file mode 100644 index 0b6dca80b5c741e04b482fbaa49c216ee8f9812d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_10.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 52.5, - "tolerance": 2.625 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 46.0, - "tolerance": 2.3000000000000003 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 35.0, - "tolerance": 1.75 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is HMC_05.edf. The three longest uninterrupted sleep episodes are 52.500000, 46.000000, and 35.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_11.json b/sleep_assessment/cases/case8/case8_11.json deleted file mode 100644 index 5fce9053817c7498fe87c6201aa56cc8cddd8fd1..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_11.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 60.5, - "tolerance": 3.0250000000000004 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 43.5, - "tolerance": 2.1750000000000003 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 31.5, - "tolerance": 1.5750000000000002 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_01.edf. The three longest uninterrupted sleep episodes are 60.500000, 43.500000, and 31.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_12.json b/sleep_assessment/cases/case8/case8_12.json deleted file mode 100644 index 0cd9f6bfb38a67ba7934ce94d2e6d16a4466d08e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_12.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 66.5, - "tolerance": 3.325 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 37.5, - "tolerance": 1.875 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 28.5, - "tolerance": 1.425 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_02.edf. The three longest uninterrupted sleep episodes are 66.500000, 37.500000, and 28.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_13.json b/sleep_assessment/cases/case8/case8_13.json deleted file mode 100644 index 60a2166699b0f59e40fd6459580f526d2933804b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_13.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 52.5, - "tolerance": 2.625 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 51.5, - "tolerance": 2.575 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 38.5, - "tolerance": 1.925 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_03.edf. The three longest uninterrupted sleep episodes are 52.500000, 51.500000, and 38.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_14.json b/sleep_assessment/cases/case8/case8_14.json deleted file mode 100644 index 14d60fbd890bcfccb5272046a4f664d82e83e288..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_14.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 50.5, - "tolerance": 2.5250000000000004 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 50.0, - "tolerance": 2.5 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 42.0, - "tolerance": 2.1 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_04.edf. The three longest uninterrupted sleep episodes are 50.500000, 50.000000, and 42.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_15.json b/sleep_assessment/cases/case8/case8_15.json deleted file mode 100644 index 17cf9b3605a2e7f8e994177cbf732316412cd076..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_15.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 61.0, - "tolerance": 3.0500000000000003 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 39.0, - "tolerance": 1.9500000000000002 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 29.0, - "tolerance": 1.4500000000000002 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is SHHS1_05.edf. The three longest uninterrupted sleep episodes are 61.000000, 39.000000, and 29.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_16.json b/sleep_assessment/cases/case8/case8_16.json deleted file mode 100644 index f2b07554ea00b2fea3e1f6a05d1c07f350c5296f..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_16.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 164.0, - "tolerance": 8.200000000000001 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 76.0, - "tolerance": 3.8000000000000003 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 56.5, - "tolerance": 2.825 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_01.edf. The three longest uninterrupted sleep episodes are 164.000000, 76.000000, and 56.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_17.json b/sleep_assessment/cases/case8/case8_17.json deleted file mode 100644 index 344364afdf3dd5e04323cdf7b1a3368652f0a561..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_17.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 77.0, - "tolerance": 3.85 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 71.0, - "tolerance": 3.5500000000000003 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 45.0, - "tolerance": 2.25 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_02.edf. The three longest uninterrupted sleep episodes are 77.000000, 71.000000, and 45.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_18.json b/sleep_assessment/cases/case8/case8_18.json deleted file mode 100644 index 31cf79d147b6f9d7c4b02bc09a307c4d4c77c449..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_18.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 101.0, - "tolerance": 5.050000000000001 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 76.5, - "tolerance": 3.825 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 62.0, - "tolerance": 3.1 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_03.edf. The three longest uninterrupted sleep episodes are 101.000000, 76.500000, and 62.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_19.json b/sleep_assessment/cases/case8/case8_19.json deleted file mode 100644 index 6dcda708c2c1ffd163abcf96a0ce318ea0579b07..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_19.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 174.0, - "tolerance": 8.700000000000001 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 161.5, - "tolerance": 8.075000000000001 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 48.5, - "tolerance": 2.4250000000000003 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_04.edf. The three longest uninterrupted sleep episodes are 174.000000, 161.500000, and 48.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_20.json b/sleep_assessment/cases/case8/case8_20.json deleted file mode 100644 index 7625cf3a48efb3797c17f2e1897e63812d19f1d9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_20.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 87.5, - "tolerance": 4.375 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 54.0, - "tolerance": 2.7 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 51.0, - "tolerance": 2.5500000000000003 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is MASSSS3_05.edf. The three longest uninterrupted sleep episodes are 87.500000, 54.000000, and 51.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because the second and third longest episodes are also at least 40 minutes, the response must also say that overall sleep continuity is relatively stable. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_21.json b/sleep_assessment/cases/case8/case8_21.json deleted file mode 100644 index b3af72a979b27f7908593fa052c158b86946c2c9..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_21.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 41.5, - "tolerance": 2.075 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 40.0, - "tolerance": 2.0 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 31.0, - "tolerance": 1.55 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_01.edf. The three longest uninterrupted sleep episodes are 41.500000, 40.000000, and 31.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_22.json b/sleep_assessment/cases/case8/case8_22.json deleted file mode 100644 index 6dd1d2e2f03bf29e4288de1e2b650b25ebde2b0e..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_22.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 82.5, - "tolerance": 4.125 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 26.0, - "tolerance": 1.3 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 21.5, - "tolerance": 1.075 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_02.edf. The three longest uninterrupted sleep episodes are 82.500000, 26.000000, and 21.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_23.json b/sleep_assessment/cases/case8/case8_23.json deleted file mode 100644 index f49035c8ff14c92f24d005213749c72ab6cc4708..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_23.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 43.0, - "tolerance": 2.15 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 38.0, - "tolerance": 1.9000000000000001 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 36.5, - "tolerance": 1.8250000000000002 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_03.edf. The three longest uninterrupted sleep episodes are 43.000000, 38.000000, and 36.500000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that sleep continuity is moderate or average. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_24.json b/sleep_assessment/cases/case8/case8_24.json deleted file mode 100644 index 33560cb84b25cab8cc5ef9762c7042f96f497392..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_24.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 141.0, - "tolerance": 7.050000000000001 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 55.0, - "tolerance": 2.75 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 36.0, - "tolerance": 1.8 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_04.edf. The three longest uninterrupted sleep episodes are 141.000000, 55.000000, and 36.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case8/case8_25.json b/sleep_assessment/cases/case8/case8_25.json deleted file mode 100644 index 925003870decbada784da3c858347767b134ece0..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case8/case8_25.json +++ /dev/null @@ -1,56 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1.5, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to identify the three longest uninterrupted sleep episodes across the whole night and report their lengths in descending order. Then briefly interpret what these episodes suggest about the overall sleep continuity of the night." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract the lengths of the three longest uninterrupted sleep episodes in minutes.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"top1_sleep_episode_min\", \"top2_sleep_episode_min\", and \"top3_sleep_episode_min\".\n5. All three values must be FLOATS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"top1_sleep_episode_min\": , \"top2_sleep_episode_min\": , \"top3_sleep_episode_min\": }", - "metrics": [ - { - "metric_id": "top1_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top1_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 207.0, - "tolerance": 10.350000000000001 - } - }, - { - "metric_id": "top2_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top2_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 170.5, - "tolerance": 8.525 - } - }, - { - "metric_id": "top3_sleep_episode_accuracy", - "type": "numeric_check", - "target_key": "top3_sleep_episode_min", - "weight": 20, - "params": { - "gt_value": 33.0, - "tolerance": 1.6500000000000001 - } - }, - { - "metric_id": "sleep_continuity_semantic_consistency", - "type": "semantic_consistency", - "target_key": "full_text", - "weight": 40, - "params": { - "judge_prompt": "You are judging semantic consistency for a sleep-medicine benchmark. The sample is Physionet2018_05.edf. The three longest uninterrupted sleep episodes are 207.000000, 170.500000, and 33.000000 minutes. The benchmark rule is: if the longest episode is <20 minutes, sleep is severely fragmented; 20-40 minutes means poor continuity; 40-90 minutes means moderate continuity; >=90 minutes means at least one good continuous sleep episode. The correct response must explicitly conclude that there is at least one clearly good continuous sleep episode. Because only the longest episode is clearly long while the other long episodes are shorter, the response must also note that the overall night still shows some fragmentation. Score the response in two parts. Give 0.5 points if the base continuity judgement is correct. Give 0.5 points if the secondary stability-or-fragmentation judgement is correct. The total score must therefore be exactly one of 0.0, 0.5, or 1.0. Output only a valid JSON object with exactly one key named score whose value is 0.0, 0.5, or 1.0. Do not output Markdown, explanations, or additional fields." - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_01.json b/sleep_assessment/cases/case9/case9_01.json deleted file mode 100644 index d1c7ca57b62be350fdc4f46a95370ce290070fd8..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_01.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 133, - "tolerance": 3 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 3, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_02.json b/sleep_assessment/cases/case9/case9_02.json deleted file mode 100644 index 9473ad1bc02ebbca55a8665e60f2234b47a2143a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_02.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 180, - "tolerance": 5 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 3, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_03.json b/sleep_assessment/cases/case9/case9_03.json deleted file mode 100644 index 94e7d6e59d97aacfcab00c92ad75c2bb13ddbbc5..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_03.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 184, - "tolerance": 5 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 1, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_04.json b/sleep_assessment/cases/case9/case9_04.json deleted file mode 100644 index a7fff26ee19328db50a1d46d155badb01d83e3b7..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_04.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 123, - "tolerance": 3 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_05.json b/sleep_assessment/cases/case9/case9_05.json deleted file mode 100644 index 02553e77521ecf4186883aa33d31294aa72a9600..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_05.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "ISRUC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "ISRUC" - }, - "agent_input": { - "label_path": "data/sleep/ISRUC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 123, - "tolerance": 3 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_06.json b/sleep_assessment/cases/case9/case9_06.json deleted file mode 100644 index f8202b9b5f2abe81fe192447cb543b460cc6bc33..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_06.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 98, - "tolerance": 2 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_07.json b/sleep_assessment/cases/case9/case9_07.json deleted file mode 100644 index 170dedfe84d8b26efd5a811a99c336a1a0b2497a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_07.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 111, - "tolerance": 3 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_08.json b/sleep_assessment/cases/case9/case9_08.json deleted file mode 100644 index 5fcfad8cf8aaa9a8980f7b18dfd273b664248d00..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_08.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 151, - "tolerance": 4 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_09.json b/sleep_assessment/cases/case9/case9_09.json deleted file mode 100644 index 9f8850b1d16f769a0bfaa862143a8cd3b3201870..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_09.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 135, - "tolerance": 4 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_10.json b/sleep_assessment/cases/case9/case9_10.json deleted file mode 100644 index a521e0b3530d945f38c808aa6a7c128503294378..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_10.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "HMC_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "HMC" - }, - "agent_input": { - "label_path": "data/sleep/HMC_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 249, - "tolerance": 7 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 2, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_11.json b/sleep_assessment/cases/case9/case9_11.json deleted file mode 100644 index cd891995c7a0e1a463455411ebdc503346c41fed..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_11.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 180, - "tolerance": 5 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_12.json b/sleep_assessment/cases/case9/case9_12.json deleted file mode 100644 index fcd04d85c0e1011a446f625ce53a796a5c74865c..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_12.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 95, - "tolerance": 2 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_13.json b/sleep_assessment/cases/case9/case9_13.json deleted file mode 100644 index 3b6acd059f4a2cd7f2d21d6db419d0aad535eabc..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_13.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 143, - "tolerance": 4 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 6, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_14.json b/sleep_assessment/cases/case9/case9_14.json deleted file mode 100644 index d656123a15d5d4153f40efd4dbacd418362a5d2d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_14.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 138, - "tolerance": 4 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 4, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_15.json b/sleep_assessment/cases/case9/case9_15.json deleted file mode 100644 index 07a7c23619c7cb14e5a3bd2289f5c19754786055..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_15.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "SHHS1_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "SHHS1" - }, - "agent_input": { - "label_path": "data/sleep/SHHS1_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 133, - "tolerance": 3 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 2, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_16.json b/sleep_assessment/cases/case9/case9_16.json deleted file mode 100644 index 078da77d1dc8837186f9ba616f9cff24574b83bd..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_16.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 179, - "tolerance": 5 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_17.json b/sleep_assessment/cases/case9/case9_17.json deleted file mode 100644 index d16cb749fa1df7b60f3dc7c7c1885fdf2833d093..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_17.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 195, - "tolerance": 5 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 3, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_18.json b/sleep_assessment/cases/case9/case9_18.json deleted file mode 100644 index fe55487c768e864d5e2cf141db904c80fbaddeaf..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_18.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 144, - "tolerance": 4 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 1, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_19.json b/sleep_assessment/cases/case9/case9_19.json deleted file mode 100644 index 6158b94b18ef94a899a44b366cbd4f5daaad889d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_19.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 100, - "tolerance": 3 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_20.json b/sleep_assessment/cases/case9/case9_20.json deleted file mode 100644 index fb7f9abb83cffbf315c2847675aa336b9b40b95b..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_20.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "MASSSS3_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "MASSSS3" - }, - "agent_input": { - "label_path": "data/sleep/MASSSS3_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 117, - "tolerance": 3 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 2, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_21.json b/sleep_assessment/cases/case9/case9_21.json deleted file mode 100644 index e61165d392fdce717f1b91d4e48cf19a66ba0e59..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_21.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_01.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_01.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 99, - "tolerance": 2 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_22.json b/sleep_assessment/cases/case9/case9_22.json deleted file mode 100644 index c59dacadbbef97e788a4c8c6a5df50af347bfada..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_22.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_02.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_02.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 101, - "tolerance": 3 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_23.json b/sleep_assessment/cases/case9/case9_23.json deleted file mode 100644 index 28a916acd2105dbd343801e9bba3ca588e24555d..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_23.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_03.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_03.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 84, - "tolerance": 2 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 1, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_24.json b/sleep_assessment/cases/case9/case9_24.json deleted file mode 100644 index 0509f2cc514c408a2fae6f53bcf5a6ced7d6f41a..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_24.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_04.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_04.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 155, - "tolerance": 4 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 1, - "tolerance": 0 - } - } - ] - } -} diff --git a/sleep_assessment/cases/case9/case9_25.json b/sleep_assessment/cases/case9/case9_25.json deleted file mode 100644 index e9c6a741e9102c10217bf193ede5539b05b37a14..0000000000000000000000000000000000000000 --- a/sleep_assessment/cases/case9/case9_25.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "meta_info": { - "case_id": "Physionet2018_05.edf", - "bench_subset": "NeuroBench-Sleep", - "difficult": 1, - "original_dataset": "Physionet2018" - }, - "agent_input": { - "label_path": "data/sleep/Physionet2018_05.npy", - "instruction": "Please use the provided sleep-stage label file to calculate two transition-based metrics for the whole night: the total number of sleep-stage transitions, and the number of interruptions from deep sleep into wakefulness. Please clearly report both final counts." - }, - "eval_config": { - "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two transition counts from the agent's report:\n1) the total number of sleep-stage transitions across the whole night\n2) the number of deep-sleep interruptions into wakefulness\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"total_stage_transitions\" and \"n3_to_w_interruptions\".\n5. Both values must be INTEGERS or null.\n6. If one value is missing or cannot be found, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"total_stage_transitions\": , \"n3_to_w_interruptions\": }", - "metrics": [ - { - "metric_id": "total_stage_transition_accuracy", - "type": "numeric_check", - "target_key": "total_stage_transitions", - "weight": 50, - "params": { - "gt_value": 83, - "tolerance": 2 - } - }, - { - "metric_id": "n3_to_w_interruptions_accuracy", - "type": "numeric_check", - "target_key": "n3_to_w_interruptions", - "weight": 50, - "params": { - "gt_value": 0, - "tolerance": 0 - } - } - ] - } -}